{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/abulbasar/anaconda/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "/Users/abulbasar/anaconda/lib/python3.6/site-packages/sklearn/grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n",
      "/Users/abulbasar/anaconda/lib/python3.6/site-packages/sklearn/learning_curve.py:22: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the functions are moved. This module will be removed in 0.20\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import *\n",
    "import scipy\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Datasource: http://yann.lecun.com/exdb/mnist/\n",
    "\n",
    "The training dataset consists of 60,000 training digits and the test set contains 10,000 samples, respectively. The images in the MNIST dataset consist of  pixels, and each pixel is represented by a gray scale intensity value. Here, we unroll the  pixels into 1D row vectors, which represent the rows in our image array (784 per row or image). The second array (labels) returned by the load_mnist function contains the corresponding target variable, the class labels (integers 0-9) of the handwritten digits.\n",
    "\n",
    "\n",
    "Csv version of the files are available in the following links.\n",
    "CSV training set http://www.pjreddie.com/media/files/mnist_train.csv\n",
    "CSV test set http://www.pjreddie.com/media/files/mnist_test.csv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "training = pd.read_csv(\"/data/MNIST/mnist_train.csv\", header = None)\n",
    "testing = pd.read_csv(\"/data/MNIST/mnist_test.csv\", header = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = training.iloc[:, 1:].values, training.iloc[:, 0].values \n",
    "X_test, y_test = testing.iloc[:, 1:].values, testing.iloc[:, 0].values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train:  (60000, 784) shape of y_train:  (60000,)\n",
      "Shape of X_test:  (10000, 784) shape of y_test:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of X_train: \", X_train.shape, \"shape of y_train: \", y_train.shape)\n",
    "print(\"Shape of X_test: \", X_test.shape, \"shape of y_test: \", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of class frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Frequency (normed)')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a0ihJuwGTgTkd6swBzky3TwXuiYgA7gIOldSYJpF/AFbmfVNmZtaz8qxncShwFvBx4G7g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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3136cdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_counts = pd.DataFrame({\n",
    "    \"train\": pd.Series(y_train).value_counts().sort_index(), \n",
    "    \"test\": pd.Series(y_test).value_counts().sort_index()\n",
    "})\n",
    "(label_counts / label_counts.sum()).plot.bar()\n",
    "\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Frequency (normed)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chisquare test on class frequencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=250122.18994735854, pvalue=0.0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.chisquare(label_counts.train, label_counts.test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display a few sample images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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ZPWpm81K/8T6zvscLIQwNIbwVQlgRQpgqaZyk3XJ4SkBBFKF2XgshjJL0SS7P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\n1Pn/uibjzIO/SngAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a17fc1518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(5, 5, figsize = (15, 9))\n",
    "for i, ax in enumerate(fig.axes):\n",
    "    img = X_train[i, :].reshape(28, 28)\n",
    "    ax.imshow(img, cmap = \"Greys\", interpolation=\"nearest\")\n",
    "    ax.set_title(\"True: %i\" % y_train[i])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View different variations of a digit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ss3N6/kx7qDMdf8wxx5js/zxiTzWKyebNm01+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\nJj/77LMFmgRAVP59qocNG1agSYDM5s+fb/Ixxxxj8vLly9M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AACAQ\ni2oAAAAAAAKV9Z7qTPbYYw+TX3rppQJNAiRf586dTfb3jAII9/jjj5vsX8MAQMs9/PDDJn/jG98w\n+dFHHzVFlvjkAAAgAElEQVTZv4/v5ZdfnpvBgBx4//33Tc60h3rkyJEmV1VVxT1SSeKVagAAAAAA\nArGoBgAAAAAgEItqAAAAAAAClfWe6oEDB5rMHlAAQD4cdthhJjc0NBRoEqD8dOnSxeS5c+cWaBIg\n9wYNGmQy653c4JVqAAAAAAACsagGAAAAACAQi2oAAAAAAAKpcy5/J1OtF5GVItJdRNbn7cTRMd+n\nDXTOVeb5nGiE/sSG/pQh+hObQs1HhwqI/sSG/pQh+hObRPcnr4vqT06qWuucq877iVuI+ZBkSf/6\nMx+SLOlff+ZDkiX96898SLKkf/2ZLzu8/RsAAAAAgEAsqgEAAAAACFSoRfXUAp23pZgPSZb0rz/z\nIcmS/vVnPiRZ0r/+zIckS/rXn/myUJA91QAAAAAAlALe/g0AAAAAQCAW1QAAAAAABMrrolpVT1TV\n11T1DVWtyee5m5lnuqquU9V/NvpYN1V9UlWXp/7sWsD5+qvqX1R1maouVdVLkzYj8idp/RFJdofo\nDxqjP5Fnoz/4BP2JPBv9wSfoT+TZirI/eVtUq2qFiPxcRE4SkQNFZKSqHpiv8zdjhoic6H2sRkSe\ncs7tIyJPpXKhNIjIlc65A0TkSBEZl/r/LEkzIg8S2h+RZHeI/kBE6E8g+gMRoT+B6A9EhP4EKsr+\n5POV6i+IyBvOuRXOuR0i8qCIDM/j+T/FOfeciGzwPjxcRGam/j5TRE7N61CNOOfqnHOLU3/fLCLL\nRKSvJGhG5E3i+iOS7A7RHzRCfyKiP2iE/kREf9AI/YmoWPuTz0V1XxF5p1FenfpY0vR0ztWJfPxF\nFZEeBZ5HRERUtUpEDheRlyShMyKniqU/Ign8/qQ/ZY/+ZIH+lD36kwX6U/boTxaKqT/5XFRrEx/j\nfl4toKqdRGS2iFzmnNtU6HlQEPQnEP2B0J9g9AdCf4LRHwj9CVZs/cnnonq1iPRvlPuJyJo8nr+l\n1qpqbxGR1J/rCjmMqraRj7+h7nfOPZL6cKJmRF4US39EEvT9SX+QQn8C0B+k0J8A9Acp9CdAMfYn\nn4vqhSKyj6oOUtW2IjJCRObl8fwtNU9ERqf+PlpE5hZqEFVVEZkmIsucc5MbfSoxMyJviqU/Ign5\n/qQ/aIT+RER/0Aj9iYj+oBH6E1Gx9kedy987EFR1mIj8REQqRGS6c+6WvJ286XkeEJFjRaS7iKwV\nkRtEZI6IzBKRASKySkTOdM75G/nzNd/RIvJXEVkiIrtSH75WPt5XkIgZkT9J649IsjtEf9AY/Yk8\nG/3BJ+hP5NnoDz5BfyLPVpT9yeuiGgAAAACAUpLPt38DAAAAAFBSWFQDAAAAABCIRTUAAAAAAIFY\nVAMAAAAAEIhFNQAAAAAAgVhUAwAAAAAQiEU1AAAAAACBWFQDAAAAABCIRTUAAAAAAIFYVAMAAAAA\nEIhFNQAAAAAAgVhUAwAAAAAQiEU1AAAAAACBslpUq+qJqvqaqr6hqjVxDQWUA/oDhKM/QDj6A4Sj\nP2iKOufCHqhaISKvi8hQEVktIgtFZKRz7l/NPaZ79+6uqqoq6HworLffflvWr1+vhZ6jVNCf8kJ/\n4kV/ys+iRYvWO+cqCz1HKaA/5Yf+xIf+lJ+W9qd1Fuf4goi84ZxbISKiqg+KyHARafabqqqqSmpr\na7M4JQqlurq60COUGvpTRuhP7OhPmVHVlYWeoYTQnzJDf2JFf8pMS/uTzdu/+4rIO43y6tTH/EHG\nqmqtqtbW19dncTqgpNAfIBz9AcLRHyAc/UGTsllUN/VWxk+9l9w5N9U5V+2cq66s5J0nQAr9AcLR\nHyAc/QHC0R80KZtF9WoR6d8o9xORNdmNA5QN+gOEoz9AOPoDhKM/aFI2i+qFIrKPqg5S1bYiMkJE\n5sUzFlDy6A8Qjv4A4egPEI7+oEnBFypzzjWo6ngReVxEKkRkunNuaWyTASWM/gDh6A8Qjv4A4egP\nmpPN1b/FOfeYiDwW0yxAWaE/QDj6A4SjP0A4+oOmZPP2bwAAAAAAyhqLagAAAAAAArGoBgAAAAAg\nEItqAAAAAAACsagGAAAAACAQi2oAAAAA+P/Yu+8Aqaqzj+PPQ69iYenERQXUF1sCGoNEIxLFErDw\nKrGgYokRJUoSV2LFmJAiWFGIIKBG0EAAjahIbG8EBYkNEUGi9CZBUAEFzvsHE7PPkb2zc6bdmfl+\n/oHfzp25j+7+3D3OnnuBQCyqAQAAAAAIxKIaAAAAAIBALKoBAAAAAAjEohoAAAAAgEAsqgEAAAAA\nCMSiGgAAAACAQCyqAQAAAAAIxKIaAAAAAIBALKoBAAAAAAjEohoAAAAAgEC18j0AAAAAACD+Fi5c\naPLgwYNNnjx5cuTznXMZnykOeKcaAAAAAIBALKoBAAAAAAjEohoAAAAAgEDsqY6w3377mfzxxx+b\nfM0115g8cOBAk9u2bZvS+f75z3+a7O9Z8PcgHHTQQSYffvjhKZ0PyKRVq1aZ7H89XnHFFSY/9NBD\nJi9dujTy9f2++Xt4Fi9ebPLbb79t8imnnGJy69atI88HxNlHH31ksv/1/8gjj5g8fvz4yNfzv7/0\n7dvX5LFjx5pcp06dakwJZMf06dNN9v/77n89//a3vzX5vPPOM/nDDz+MPN+xxx6b0nwrV640+a23\n3jK5Vi3743ePHj1Sen0gl0aMGGHylVdemadJ4o13qgEAAAAACMSiGgAAAACAQCyqAQAAAAAIxJ7q\nStavX2/yli1bTFZVk++8806Tx40bZ3Ljxo0jn+9bt26dyV988YXJ/h4hf4/qvffeG/n6QDZ1797d\n5FatWpm8YsUKk5ctW2Zysn74fbvnnntM9vuxc+dOk9u1a2dysj10QCr8r7ft27eb7O/x9/dE+9fQ\neOqppyLP9/rrr6c0X+3atU2uW7euyf73m4kTJ5r84IMPmsyeauTSm2++afK5555rsv/9w89Dhgwx\n+bnnnjN51qxZkefv1q1bteb8jyVLlpj8r3/9y+QaNex7Wv41C/x/PiCf2ENdPbxTDQAAAABAIBbV\nAAAAAAAEYlENAAAAAEAg9lRX8sADD5js78np2bNnSq/n7/H0X2/Dhg0m+/fBBuLsxhtvNNnfE9q1\na1eThw8fbvL3v/99kzt16mTy008/HXl+/xoEd999d+TxJ5xwQuTjQCr8PdQ33XSTyf59cTPt0EMP\nNbl9+/Ymt2jRwuRf/vKXJu+zzz4mH3DAASavXr063RGBjNm4caPJn376aeTx/vcXv5/l5eUmf+c7\n34l8/ZkzZ1ZnzGrz//vh78EG8unMM8/M6etPmjQpq+fLFd6pBgAAAAAgEItqAAAAAAACJV1Uq+oY\nVV2rqu9W+tjeqjpDVRcl/twru2MChYn+AOHoDxCO/gDh6A9SVZ091WNF5F4RGV/pYxUiMtM5N1RV\nKxL5usyPlx7/PqH+fQDnzZtn8siRI00ePHiwybfddlvmhhORzz77zORTTjnF5H/84x8mN2nSxOTL\nL788o/MgK8ZKgfYnmfHjx5vsX0Pgk08+iXz8/PPPj3z9I444IvLxZ555xuRke6r79OkT+ThiaazE\ntD/+f5+T7aH2//s9aNAgkxs2bGiyf5/aWrXst+sGDRqY7N932ud/P+zdu7fJ/h7qs88+2+R69epF\nvj5iaazEtD/J+NeYOfXUUyOPf+ihh0z2+1OzZs3I5/vX6Ni2bZvJ/jVEVqxYYfITTzwR+fq+M844\nw+Rf/epXKT0fOTFWCrQ/qfKviTN58uSUnp/sGlK+VF+/UCR9p9o597KIbPA+3EtExiX+Pk5EeguA\nb6A/QDj6A4SjP0A4+oNUhe6pbu6cWyUikvizWVUHquplqjpXVef6/ycQKFH0BwhHf4Bw9AcIR39Q\npaxfqMw5N8o519k517msrCzbpwOKCv0BwtEfIBz9AcLRn9ITep/qNara0jm3SlVbisjaTA6VKUuX\nLjX5Jz/5SUrP9+/rmWmjRo0y2d+j55s9e7bJHTp0yPhMyImC6E8yf/zjH03292D6/WncuHFa5/P3\nsPl70nwXX3yxyccee2xa50dsxKI//n3YhwwZYvIee+xh8kUXXWRyo0aNsjNYFfx3SqZPnx55vN/n\nGjW4WUiRiEV/fP6e/+HDh5u8ZcsWk9u1a2eyf42AZHuoff7x/jUL7rjjDpPfffddk1PdUz1gwACT\n6VfBiGV/UuXvofavIeX/fOXvgU7281ey5xer0BZPE5F+ib/3E5GpmRkHKAn0BwhHf4Bw9AcIR39Q\npercUusxEZklIh1Vdbmq9heRoSLSQ1UXiUiPRAbgoT9AOPoDhKM/QDj6g1Ql/fVv51zfKh7qnuFZ\ngKJDf4Bw9AcIR3+AcPQHqQrdU10Q9ttvP5MvvfRSk/09zTt37szqPLNmzTL5F7/4RUrPZw814uSw\nww4zedmyZSa3aNEio+fz7wO8devWyOMvuOACk2vXrp3ReVDa/D2Qcb/P7JQpUyIfb9OmjcnHHHNM\nNscBjLVr7dbUe+65J/J4/5oFfo6bAw88MDIDuTRz5kyTk+159vdI/+Y3v4k83n882ev7e7w7duwY\neXxccWUEAAAAAAACsagGAAAAACAQi2oAAAAAAAIV9Z5qn3+fWv++hD169DC5YcOGaZ1v/fr1Jl9y\nySUmq6rJ/n1Ln3zyybTOD2RTrvf4b9iwIfLxJk2amNyqVatsjgPE2hdffGHy7373u8jjx4wZY/I+\n++yT8ZmATHn44Ydzer41a9aY3KtXr8jj/WuO+HtY99prr8wMBlSDv2f5yiuvjDz+/fffNznVPc7+\n8cnuW+33I9XXjwveqQYAAAAAIBCLagAAAAAAArGoBgAAAAAgUEntqT7nnHMic6Z17drV5A8//DDy\n+Pvvv9/kbt26ZXwmoFAsX77c5KeffjryeH/PzgEHHJDxmYBCsXLlSpP9+8j7unTpks1xgEj+Hv5B\ngwaZvGXLFpP333//rM7jX8PjhBNOMPmjjz6KfP6Pf/xjk9lDjXzK933Ru3fvbrK/p9rf4+3/PDdp\n0qTsDJZhvFMNAAAAAEAgFtUAAAAAAARiUQ0AAAAAQKCS2lOdbVu3bjV58eLFJvv3pT700ENNPuWU\nU7IzGFAA/D1zFRUVJm/atMnk//mf/zH5vvvuy85gQIBVq1aZPGHCBJOPPvpok/09b/PmzUvr/BMn\nTkzp+L/97W8mN2/ePKXnd+rUyeRmzZql9HyUtrp165r8+9//Pk+T7OLv6X7vvfcij69fv77J/DyH\nfDrzzDNTOt7/+SnT94H291QXK96pBgAAAAAgEItqAAAAAAACsagGAAAAACAQe6rT4O+hPvnkk1N6\n/s9+9jOT99hjj7RnAgrVSy+9ZPKf//znyOOPPPJIk+vVq5fxmYBQF198scnPPfdc5PE1atj/x71z\n586MzxTlvPPOS+v5jz/+uMmp7ukD8mn8+PEm+1/PvhYtWpg8ffp0kw866KDMDAZUw4gRI0z27wOd\nb6nu0Y7b/NXFO9UAAAAAAARiUQ0AAAAAQCAW1QAAAAAABGJPdQo2b95scs+ePU2eNWuWyf6euJ/+\n9KcmX3DBBRmcDigsK1asMPmWW26JPP7ss882+Z577sn0SEDG+PfZPeqoo1J6vn+NgdmzZ0cev337\ndpOT7cmuWbNmZD788MNNbtq0qcnf+c53TD7xxBMjzwfEybZt20yuqKgw2b9mjq9Xr14mH3rooZkZ\nDAjg3wf6/fffN3nmzJmR2V+fxI1/jY5JkyblaZJovFMNAAAAAEAgFtUAAAAAAARiUQ0AAAAAQCD2\nVEfYtGmTyU888YTJ/h43VTXZ35N2++23Z3A6oLD4e9ROO+00k998802T/fu2+/1p0KBBBqcDMuuQ\nQw6JzJk2bdo0k08//XST/b4sW7bM5D333DM7gwEx4H//8e/LvmbNmsjn+9+vhg0blpnBgAxIdh9o\n//Fc76FeuHBhWs//zW9+k6FJsot3qgEAAAAACMSiGgAAAACAQCyqAQAAAAAIxJ7qCPPnzzf58ssv\nT+n5L774oslNmjRJdySgYD3//PMmJ9tDPWXKFJP322+/7AwGFIG5c+dGPt64cWOT2UONUvLzn//c\n5L/+9a+Rx7do0cLkCRMmmFyvXr3MDAYUIX8P9eDBg00+44wzTJ48ebLJ/n22k+0ZjwveqQYAAAAA\nIBCLagAAAAAAAiVdVKtqW1V9QVUXqOp8VR2Y+PjeqjpDVRcl/twr++MChYX+AOHoDxCO/gDh6A9S\nVZ091dtFZJBzbp6qNhaRN1R1hohcKCIznXNDVbVCRCpE5LrsjZp7I0eOjHy8Zs2aJt9www0ms4ca\nUsL9WbJkickXXHBB5PE9evQw+bjjjsv0SCg8JdufZP7973+bfP/990ceP3369GyOg3gqmf589dVX\nJi9atMhk/xodvjp16pg8ZMgQk9lDXZJKpj+Z5u+h9vdMO+dMVlWTC2UPtS/pO9XOuVXOuXmJv28W\nkQUi0lpEeonIuMRh40Skd7aGBAoV/QHC0R8gHP0BwtEfpCqlPdWqWi4iR4jIayLS3Dm3SmTXF56I\nNKviOZep6lxVnbtu3br0pgUKGP0BwtEfIBz9AcLRH1RHtRfVqtpIRCaJyM+cc5uq+zzn3CjnXGfn\nXOeysrKQGYGCR3+AcPQHCEd/gHD0B9VVrftUq2pt2fUF9ahz7j+/GL9GVVs651apaksRWZutIXPl\ngw8+MHnatGmRx/t7qG+88caMz4TCVyr98a1Zs8bkjRs3Rh6/115c6wPfVKr9Sea1114zecOGDSa3\na9fO5Pbt22d9JsRPqfRn+PDhJl9//fUpPd+/D3WvXr3SngmFr1T6ky7/vtT+HmrfmWeeabJ/X+pC\nVZ2rf6uIjBaRBc65YZUemiYi/RJ/7yciUzM/HlDY6A8Qjv4A4egPEI7+IFXVeae6q4icLyLvqOqb\niY8NFpGhIvK4qvYXkaUi0ic7IwIFjf4A4egPEI7+AOHoD1KSdFHtnPs/EdEqHu6e2XGA4kJ/gHD0\nBwhHf4Bw9Aepqtae6lLh3xetRg372/Ft27Y1uX///lmfCSgU/n1Cx48fH3m8f99P/xoFAKo2a9as\nyMc7depkcoMGDbI5DpBT/vebZcuWpfT8Dh06mHziiSemPROA6pk0aVK+R8iKlG6pBQAAAAAA/otF\nNQAAAAAAgVhUAwAAAAAQqKT3VD/wwAMm+3uo999/f5Ovvvpqk1u1apWdwYAC5Pdj5MiRkcf79wX9\n1re+lfGZgGKxbds2k/378voGDRqUzXGAnNq5c6fJ/tf/iBEjIp+/6+5I/1VRUWGyf40PAFXz+zZz\n5szI488444xsjhMbvFMNAAAAAEAgFtUAAAAAAARiUQ0AAAAAQKCS2lP98ccfm/y73/0u8vgf/vCH\nJg8YMCDjMwGF6l//+pfJ/h5p35FHHmly9+7dMz4TUKz+8Y9/mPz555+bXKdOHZO7deuW9ZmAXHn5\n5ZdNvv7661N6vn+NgX79+qU9E4BdJk+ebLK/h7pY70vt451qAAAAAAACsagGAAAAACAQi2oAAAAA\nAAKV1J5qfw/1smXL8jQJUPiGDRtm8qeffmqyf9/3X/3qVyY3bNgwO4MBRei+++6LfPzkk0/O0SRA\n/JWVlZn805/+NE+TAMUn2TVxSmUPtY93qgEAAAAACMSiGgAAAACAQCyqAQAAAAAIVFJ7qi+77DKT\n161bZ7J/n7Wzzz476zMBhaqiosLkxx57zOT//d//Nfm0007L+kxAsfLvyzt16lSTDznkkFyOA8Ta\nM888Y/K+++6bp0mA4tOxY0eTnXN5miReeKcaAAAAAIBALKoBAAAAAAjEohoAAAAAgEAltaf68MMP\nN/mJJ57I0yRA4WvdurXJ69evz9MkQPHr3Lmzydu3b8/TJEDuHXfccSbv2LEjP4MAQBV4pxoAAAAA\ngEAsqgEAAAAACMSiGgAAAACAQJrLe4up6joR+VhEmopInDdgMt837eucK8vxOVEJ/ckY+lOC6E/G\n5Gs+OpRH9Cdj6E8Joj8ZE+v+5HRR/fVJVec65zonPzI/mA9xFvfPP/MhzuL++Wc+xFncP//MhziL\n++ef+dLDr38DAAAAABCIRTUAAAAAAIHytagelafzVhfzIc7i/vlnPsRZ3D//zIc4i/vnn/kQZ3H/\n/DNfGvKypxoAAAAAgGLAr38DAAAAABCIRTUAAAAAAIFyuqhW1ZNUdaGqLlbVilyeu4p5xqjqWlV9\nt9LH9lbVGaq6KPHnXnmcr62qvqCqC1R1vqoOjNuMyJ249Uck3h2iP6iM/qQ8G/3B1+hPyrPRH3yN\n/qQ8W0H2J2eLalWtKSL3iUhPETlYRPqq6sG5On8VxorISd7HKkRkpnOuvYjMTOR82S4ig5xzB4nI\nd0XkysS/szjNiByIaX9E4t0h+gMRoT+B6A9EhP4Eoj8QEfoTqCD7k8t3qo8UkcXOuSXOuS9FZIKI\n9Mrh+b/BOfeyiGzwPtxLRMYl/j5ORHrndKhKnHOrnHPzEn/fLCILRKS1xGhG5Ezs+iMS7w7RH1RC\nf1JEf1AJ/UkR/UEl9CdFhdqfXC6qW4vIskp5eeJjcdPcObdKZNcnVUSa5XkeERFR1XIROUJEXpOY\nzoisKpT+iMTw65P+lDz6kwb6U/LoTxroT8mjP2kopP7kclGtu/kY9/OqBlVtJCKTRORnzrlN+Z4H\neUF/AtEfCP0JRn8g9CcY/YHQn2CF1p9cLqqXi0jbSrmNiKzM4fmra42qthQRSfy5Np/DqGpt2fUF\n9ahzbnLiw7GaETlRKP0RidHXJ/1BAv0JQH+QQH8C0B8k0J8AhdifXC6q54hIe1Vtp6p1ROQcEZmW\nw/NX1zQR6Zf4ez8RmZqvQVRVRWS0iCxwzg2r9FBsZkTOFEp/RGLy9Ul/UAn9SRH9QSX0J0X0B5XQ\nnxQVan/Uudz9BoKqniwid4pITREZ45y7PWcn3/08j4nIcSLSVETWiMjNIjJFRB4XkW+JyFIR6eOc\n8zfy52q+Y0TkFRF5R0R2Jj48WHbtK4jFjMiduPVHJN4doj+ojP6kPBv9wdfoT8qz0R98jf6kPFtB\n9ieni2oAAAAAAIpJLn/9GwAAAACAosKiGgAAAACAQCyqAQAAAAAIxKIaAAAAAIBALKoBAAAAAAjE\nohoAAAAAgEAsqgEAAAAACMSiGgAAAACAQCyqAQAAAAAIxKIaAAAAAIBALKoBAAAAAAjEohoAAAAA\ngEAsqgEAAAAACJTWolpVT1LVhaq6WFUrMjUUUAroDxCO/gDh6A8Qjv5gd9Q5F/ZE1Zoi8oGI9BCR\n5SIyR0T6Oufeq+o5TZs2deXl5UHnQ3599NFHsn79es33HMWC/pQW+pNZ9Kf0vPHGG+udc2X5nqMY\n0J/SQ38yh/6Unur2p1Ya5zhSRBY755aIiKjqBBHpJSJVflGVl5fL3Llz0zgl8qVz5875HqHY0J8S\nQn8yjv6UGFX9ON8zFBH6U2LoT0bRnxJT3f6k8+vfrUVkWaW8PPExf5DLVHWuqs5dt25dGqcDigr9\nAcLRHyAc/QHC0R/sVjqL6t39KuM3fpfcOTfKOdfZOde5rIzfPAES6A8Qjv4A4egPEI7+YLfSWVQv\nF5G2lXIbEVmZ3jhAyaA/QDj6A4SjP0A4+oPdSmdRPUdE2qtqO1WtIyLniMi0zIwFFD36A4SjP0A4\n+gOEoz/YreALlTnntqvqABF5VkRqisgY59z8jE0GFDH6A4SjP0A4+gOEoz+oSjpX/xbn3NMi8nSG\nZgFKCv0BwtEfIBz9AcLRH+xOOr/+DQAAAABASWNRDQAAAABAIBbVAAAAAAAEYlENAAAAAEAgFtUA\nAAAAAARiUQ0AAAAAQCAW1QAAAAAABGJRDQAAAABAIBbVAAAAAAAEYlENAAAAAEAgFtUAAAAAAARi\nUQ0AAAAAQCAW1QAAAAAABGJRDQAAAABAIBbVAAAAAAAEYlENAAAAAECgWvkeAEBx2Lx5s8kXXXSR\nyV27djX55z//eeTr9e7d2+Rbb73V5E6dOqU6IhAbzjmTd+zYYfLnn39u8sMPPxz5esOHDzd5yZIl\nKc1zzTXXmDxs2LCUng8AQCnjnWoAAAAAAAKxqAYAAAAAIBCLagAAAAAAAhX1nurbb7/d5BtuuMHk\n9u3bm3znnXeafMkll5g8ePDglM7ft29fk5s0aZLS830TJ040+d///nfk8VdccYXJNWvWTOv8QGWb\nNm0yuWfPnia/9tprJk+ZMsVkVY18ff/46dOnm1yvXr3I5x922GEm/+Y3vzH56KOPjnw+kE1PPvmk\nyaeffnpKz2/QoEHk4zVqRP8/81q17Lf/Xr16pXR+IJdeeuklk48//niTd+7caXKyr/9kHnnkEZP9\n71dt2rQx+ZhjjknrfEAu+de0ueOOO0xu2bKlyXPmzDF52bJlJvvXAEnX3XffbfK0adNMTjZf69at\nMzpPdfFONQAAAAAAgVhUAwAAAAAQiEU1AAAAAACBimpPtX/fz6+++spkf4+NvwfnhRdeMPmzzz4z\neeDAgSnNk+rxmda/f3+T69evn6dJUIxWrFhhsr+H2teoUSOTL730UpNfffVVk2fPnm3yl19+GZl9\nr7zyislDhgwx2d+jDeTS66+/Hvl4eXm5yUOHDjX5hz/8ocnLly83+dBDD418/csuu8zkY489NvJ4\nIJe2bNli8h/+8AeT/T3O/s93ya7Zkcx5550X+Xp169Y1uUWLFpGv5/986l8zJFlfgXT4P6/95S9/\nMdnvz7p160zu0aOHyf6e6q1bt5qc6Wsc+M/fuHGjybNmzTL5rLPOSut8oXinGgAAAACAQCyqAQAA\nAAAIxKIaAAAAAIBARbWn2t9jedttt0Ue//HHH5vs3yfU34P51FNPRb7eAw88YPL8+fMjj09m27Zt\nJvv3BU7G/+c78MAD05oHqCzZ17d/X/a//e1vJn/3u981+YsvvjDZv++h32d/D4+/RyjVvgC5dMMN\nN3MoAesAACAASURBVJjsX1PA34PZp0+fyNfz91QDhWzVqlUmP/PMMyk9//777zd57NixJvt7tv/9\n73+bvHTpUpM7dOhg8gcffGCy//OWz+/zSSedZPK8efNMTrZHG0iFf40of491Mp06dTL5k08+Mdn/\neSzb/GuO5GsPtY93qgEAAAAACMSiGgAAAACAQEkX1ao6RlXXquq7lT62t6rOUNVFiT/3yu6YQGGi\nP0A4+gOEoz9AOPqDVFVnT/VYEblXRMZX+liFiMx0zg1V1YpEvi7z40Xz74M2bNiwlJ4/ePBgk7/3\nve9FHp9sT1uyx5NZvXq1yddee63JEydOjHz+vffea3L79u3TmgcZMVZi2p90jRo1KvLx0aNHm+zv\nofY1aNAgMt99990mv/nmmyY/9thjka+PgjRWirQ/9erVM3nEiBEmT5s2LaXX27x5c+TjDRs2NLlv\n374pvT4K0lgp0v4kc84555js33f6q6++Mtnfc+rvqfZ/nlq0aFHk+UeOHGny+PHjTV67dq3Jud6T\nimoZK0XSH/8aAMm0a9fOZP/r1++P//iLL75o8i233GJymzZtTPav0XPsscdWd9RYSfpOtXPuZRHZ\n4H24l4iMS/x9nIj0zvBcQFGgP0A4+gOEoz9AOPqDVIXuqW7unFslIpL4s1lVB6rqZao6V1Xnrlu3\nLvB0QFGhP0A4+gOEoz9AOPqDKmX9QmXOuVHOuc7Ouc5lZWXZPh1QVOgPEI7+AOHoDxCO/pSe0PtU\nr1HVls65VaraUkTWJn1GFtSoYf+fQJ06dSKP32svez2BAQMGZHymdPj3pX722Wcjj/fvA3z88ceb\nXLNmzcwMhkyLRX/SddNNN5nsf70dfPDBWT3/G2+8YbJ/31Hf1Vdfnc1xkDtF0R/fgQceGJl9/jsf\nye7T6e8xTXYNERStgujPlClTUjr+9NNPN7lu3bom+z8f1q9f3+Q99tjD5FatWkWeb5999ol8/KWX\nXop8HAWrIPozZ84ck/v165fS8ysqKkz2rwHi5yuvvDIyJ/PEE0+kdHxchb5TPU1E/vMZ6iciUzMz\nDlAS6A8Qjv4A4egPEI7+oErVuaXWYyIyS0Q6qupyVe0vIkNFpIeqLhKRHokMwEN/gHD0BwhHf4Bw\n9AepSvrr3865qu670T3DswBFh/4A4egPEI7+AOHoD1IVuqc6lpo1sxfhO+SQQ0xu27atyfm+cMCn\nn35q8u23327yxo0bTd5zzz1N/vWvf21yx44dMzgdEO2YY44xefr06Vk9n7+H9NZbb4083r9vfKHe\n9xDYHf8+uKtWrYo8vnnz5tkcB8ioGTNmmOycizx+0qRJJvvXqEl2zZ10+ed7/fXXTd65c6fJ/jWB\nkv3zAVG++OILkwcNGmSyv97w+dfwOO200zIzWBVWrFhh8oQJE0z2++LnVK+5kCtZv/o3AAAAAADF\nikU1AAAAAACBWFQDAAAAABCoqPZUn3/++ZE5bvz7so0ePTry+JtvvtnkK664IuMzAXHlXzNg06ZN\nkccPHWovytmgQYOMzwTky7vvvpvS8ZdffnmWJgEy74EHHjD5lltuMfmjjz4yedq0aSY3bNgwG2N9\nbcuWLSZfdtllJk+dau+05O+h9u8b/K1vfSuD06HU+D/fXHXVVSYvXbrU5O3bt5t84YUXmpzpa06t\nXr3a5BNOOMHk9evXm+z3ZeDAgSbHtS+8Uw0AAAAAQCAW1QAAAAAABGJRDQAAAABAoKLaUx13/n12\nZ82aFXm8v6fh4osvzvhMQFzNmzfP5K1bt0Ye37VrV5ObNm2a8ZkAANm37777mvynP/3JZP/7QaNG\njbI+U2X+feEfe+yxlJ5/ww03mFyzZs20ZwL+o0+fPib36NHDZP++1q1atcrqPAMGDDB58eLFKT1/\n2LBhmRwna3inGgAAAACAQCyqAQAAAAAIxKIaAAAAAIBA7KnOIf++2TNmzIg8/tlnnzU513uGgHzq\n0qWLyaoaefx1111ncrbvUwoAyI1ateyPq/n+eWjs2LEpHe///Ne2bdsMTgNE23PPPSNzpn322Wcm\n+9eUKla8Uw0AAAAAQCAW1QAAAAAABGJRDQAAAABAIPZUZ9Hrr79u8pw5cyKPP+OMM0w++OCDMz4T\nEFfDhw83eefOnSbXqGH/H+A111xj8sknn5ydwYAC1Lx5c5MbNGiQp0mAwrdixQqTH3744ZSeP3jw\nYJNr166d9kxAXJ177rkmv/rqq5HHf/vb3zZ56NChGZ8pF3inGgAAAACAQCyqAQAAAAAIxKIaAAAA\nAIBA7KnOIH/PTc+ePU3euHGjyT/60Y9Mvu+++0xmzw2K2bZt20y+4YYbTPb3UPv3qT7uuOOyMhcQ\nR6tXrzZ5xowZkcf36NHD5L333jvjMwGlolu3biYvXbo08vg///nPJnfo0CHjMwH54t+H2t9DPW3a\nNJP9n+d8zz33nMlNmjRJY7r84Z1qAAAAAAACsagGAAAAACAQi2oAAAAAAAKxpzoNW7duNfn55583\n2d9D3bJlS5PbtWtncqNGjTI4HRAvX331lclXXXWVyf4ea99f/vIXk7kvNUqJf59p/z7U/vcbANXn\nXxPnwQcfjHzcv8aHv6f09NNPz+B0QH75651LLrnE5Kefftpkfw+1n/fZZ5/IxwtVcfxTAAAAAACQ\nByyqAQAAAAAIxKIaAAAAAIBA7KlOgb+n4PLLLzf5kUceiXz+lClTTO7cuXNmBgMKwMKFC00eM2ZM\nSs/37/teLHtwgOr45JNPTPb7BKD6tm/fbvLvfvc7k++77z6T/T3URx11lMkPPPCAyXXq1El3RCA2\nHnroIZMnTZqU0vOvvfZak/v3729y48aNwwaLGX4qBQAAAAAgEItqAAAAAAACJV1Uq2pbVX1BVReo\n6nxVHZj4+N6qOkNVFyX+3Cv74wKFhf4A4egPEI7+AOHoD1JVnT3V20VkkHNunqo2FpE3VHWGiFwo\nIjOdc0NVtUJEKkTkuuyNmn+33Xabycn2UPt7QA877LCMz4TYK9n+vPfeeyb/8Ic/TOn5I0eONLl2\n7dppz4SCU7L9ATKA/iT4e6jvv/9+k0eMGBH5/EaNGpk8ZMgQk+vXr5/GdIipkunPZ599ZvK7775r\n8i233JLS67Vp08bkgQMHmtyqVauUXq9QJH2n2jm3yjk3L/H3zSKyQERai0gvERmXOGyciPTO1pBA\noaI/QDj6A4SjP0A4+oNUpbSnWlXLReQIEXlNRJo751aJ7PrCE5FmVTznMlWdq6pz161bl960QAGj\nP0A4+gOEoz9AOPqD6qj2olpVG4nIJBH5mXNuU3Wf55wb5Zzr7JzrXFZWFjIjUPDoDxCO/gDh6A8Q\njv6guqp1n2pVrS27vqAedc5NTnx4jaq2dM6tUtWWIrI2W0Pmy+zZs03271vo6969u8mPPfaYyewJ\nLU2l0p8vv/zS5JtuusnktWuj/xGvu85uSbrkkksyMxgKWqn0J9N+8Ytf5HsExAD92eXuu+82+Ze/\n/GVKz3/yySdN7tatW9ozIf5KpT+vvfaaySeddFJar/f3v//d5GLdQ+2rztW/VURGi8gC59ywSg9N\nE5F+ib/3E5GpmR8PKGz0BwhHf4Bw9AcIR3+Qquq8U91VRM4XkXdU9c3ExwaLyFAReVxV+4vIUhHp\nk50RgYJGf4Bw9AcIR3+AcPQHKUm6qHbO/Z+IaBUPd6/i4wCE/gDpoD9AOPoDhKM/SFW19lSXCn9P\ngX9f3c8//zzy+T/4wQ9Mbty4cWYGA2Io2R7qKVOmRD6/ffv2Jl9zzTWZGQyAvPXWWyZ36tQpT5MA\nuTd9+nSTR44cabJzLvL53//+903u0qVLZgYDYmDx4sUmn3DCCSbXqJHSzaFkxowZJu+///5hgxW4\n1P6tAQAAAACAr7GoBgAAAAAgEItqAAAAAAAClfSe6m3btpl84403mpxsD/XgwYNN5r6gKCVvvvmm\nyX/84x8jj2/atKnJL7/8cuTjAMLVrVs33yMAObN+/XqTL774YpPXrVtn8q67Jf3XPffcY/JFF11k\ncr169dIdEcibFStWmJxsD3WyPdV9+tgLnh911FFpTFc8eKcaAAAAAIBALKoBAAAAAAjEohoAAAAA\ngEAltafav6/ugw8+aPLMmTMjn9+yZUuTBwwYYHKtWiX1rxMlZuXKlSafddZZkcf7e3Kuuuoqk8vK\nyjIzGIBvePLJJ01O1legkGzcuNHkrl27muzvofb5e6QPPfTQyMeBQjZ27FiT/T3WyZx++ukmjxkz\nxmT6sgvvVAMAAAAAEIhFNQAAAAAAgVhUAwAAAAAQqKQ2Aft7qq+++urI45s3b27y7NmzIx8Hilmd\nOnUis2/IkCEmV1RUZHwmoFSUl5eb7O+Rfuedd0y+++67sz0SkDcjR440efHixSb796Hu0aOHyb/9\n7W9NPvzwwzM4HRAv77//fkrH169f3+QrrrjCZPZQ7x7vVAMAAAAAEIhFNQAAAAAAgVhUAwAAAAAQ\nqKT2VL/99tspHT9x4kST27Rpk8lxgILStGlTk/09bACyx98j6n9/AvBf3bp1M/mJJ54wuVGjRrkc\nB8irG264weQJEyaYfNppp5l8/fXXm3zUUUdlZ7AiwzvVAAAAAAAEYlENAAAAAEAgFtUAAAAAAAQq\nqT3V3/ve90zesWNHniYBAABAiOuuuy4yA/ivjh07mvzVV1/laZLixjvVAAAAAAAEYlENAAAAAEAg\nFtUAAAAAAARS51zuTqa6TkQ+FpGmIrI+ZydOHfN9077OubIcnxOV0J+MoT8liP5kTL7mo0N5RH8y\nhv6UIPqTMbHuT04X1V+fVHWuc65zzk9cTcyHOIv755/5EGdx//wzH+Is7p9/5kOcxf3zz3zp4de/\nAQAAAAAIxKIaAAAAAIBA+VpUj8rTeauL+RBncf/8Mx/iLO6ff+ZDnMX98898iLO4f/6ZLw152VMN\nAAAAAEAx4Ne/AQAAAAAIxKIaAAAAAIBAOV1Uq+pJqrpQVRerakUuz13FPGNUda2qvlvpY3ur6gxV\nXZT4c688ztdWVV9Q1QWqOl9VB8ZtRuRO3PojEu8O0R9URn9Sno3+4Gv0J+XZ6A++Rn9Snq0g+5Oz\nRbWq1hSR+0Skp4gcLCJ9VfXgXJ2/CmNF5CTvYxUiMtM5115EZiZyvmwXkUHOuYNE5LsicmXi31mc\nZkQOxLQ/IvHuEP2BiNCfQPQHIkJ/AtEfiAj9CVSQ/cnlO9VHishi59wS59yXIjJBRHrl8Pzf4Jx7\nWUQ2eB/uJSLjEn8fJyK9czpUJc65Vc65eYm/bxaRBSLSWmI0I3Imdv0RiXeH6A8qoT8poj+ohP6k\niP6gEvqTokLtTy4X1a1FZFmlvDzxsbhp7pxbJbLrkyoizfI8j4iIqGq5iBwhIq9JTGdEVhVKf0Ri\n+PVJf0oe/UkD/Sl59CcN9Kfk0Z80FFJ/crmo1t18jPt5VYOqNhKRSSLyM+fcpnzPg7ygP4HoD4T+\nBKM/EPoTjP5A6E+wQutPLhfVy0WkbaXcRkRW5vD81bVGVVuKiCT+XJvPYVS1tuz6gnrUOTc58eFY\nzYicKJT+iMTo65P+IIH+BKA/SKA/AegPEuhPgELsTy4X1XNEpL2qtlPVOiJyjohMy+H5q2uaiPRL\n/L2fiEzN1yCqqiIyWkQWOOeGVXooNjMiZwqlPyIx+fqkP6iE/qSI/qAS+pMi+oNK6E+KCrU/6lzu\nfgNBVU8WkTtFpKaIjHHO3Z6zk+9+nsdE5DgRaSoia0TkZhGZIiKPi8i3RGSpiPRxzvkb+XM13zEi\n8oqIvCMiOxMfHiy79hXEYkbkTtz6IxLvDtEfVEZ/Up6N/uBr9Cfl2egPvkZ/Up6tIPuT00U1AAAA\nAADFJJe//g0AAAAAQFFhUQ0AAAAAQCAW1QAAAAAABGJRDQAAAABAIBbVAAAAAAAEYlENAAAAAEAg\nFtUAAAAAAARiUQ0AAAAAQCAW1QAAAAAABGJRDQAAAABAIBbVAAAAAAAEYlENAAAAAEAgFtUAAAAA\nAARKa1Gtqiep6kJVXayqFZkaCigF9AcIR3+AcPQHCEd/sDvqnAt7ompNEflARHqIyHIRmSMifZ1z\n71X1nKZNm7ry8vKg8yG/PvroI1m/fr3me45iQX9KC/3JLPpTet544431zrmyfM9RDOhP6aE/mUN/\nSk91+1MrjXMcKSKLnXNLRERUdYKI9BKRKr+oysvLZe7cuWmcEvnSuXPnfI9QbOhPCaE/GUd/Soyq\nfpzvGYoI/Skx9Cej6E+JqW5/0vn179YisqxSXp74mD/IZao6V1Xnrlu3Lo3TAUWF/gDh6A8Qjv4A\n4egPdiudRfXufpXxG79L7pwb5Zzr7JzrXFbGb54ACfQHCEd/gHD0BwhHf7Bb6Syql4tI20q5jYis\nTG8coGTQHyAc/QHC0R8gHP3BbqWzqJ4jIu1VtZ2q1hGRc0RkWmbGAooe/QHC0R8gHP0BwtEf7Fbw\nhcqcc9tVdYCIPCsiNUVkjHNufsYmA4oY/QHC0R8gHP0BwtEfVCWdq3+Lc+5pEXk6Q7MAJYX+AOHo\nDxCO/gDh6A92J51f/wYAAAAAoKSxqAYAAAAAIBCLagAAAAAAArGoBgAAAAAgEItqAAAAAAACsagG\nAAAAACAQi2oAAAAAAAKxqAYAAAAAIBCLagAAAAAAArGoBgAAAAAgEItqAAAAAAACsagGAAAAACAQ\ni2oAAAAAAAKxqAYAAAAAIBCLagAAAAAAArGoBgAAAAAgUK18D4D/mj9/vsllZWUmN2vWLJfjAAAA\nAACS4J1qAAAAAAACsagGAAAAACAQi2oAAAAAAAKxpzqPBg0aZPJdd91l8sCBA02+4447sj4TUF3j\nx483+aKLLkrr9Xbu3GnyiBEjTK5bt25Kr1deXm7y8ccfHzQXkA+vvvqqyV27djX5kUceMfncc89N\n63yzZ882+e233zb50UcfNXnhwoUmr169Oq3zAwBQyHinGgAAAACAQCyqAQAAAAAIxKIaAAAAAIBA\n7KnOoU8++cTk0aNHm+ycM/noo4/O+kxAdY0ZM8bkK664wmRVjXx+z549TZ4+fbrJNWrY/8d31VVX\npTqiUbNmTZP/+Mc/ZvT1gWy6+uqrTfb70a9fP5MvueQSkw844ACTO3XqZPLSpUtNfv311032r3Hg\na9asWeTjQCo+++wzk7t162byW2+9ZbL/81Ky7z/5tvfee5v88ccfm9ywYcNcjoOY27Ztm8lfffWV\nybVq2eWb3wf/+0Wq16TJNH9+//uL//jmzZtN9q8h4vO/X+brn5d3qgEAAAAACMSiGgAAAACAQCyq\nAQAAAAAIVNB7qpctW2ay/zv6mzZtMtm/r+bBBx9scrp7WtasWWOyv2ft5z//ucn+ngH/PqMnn3xy\nWvMAmTRhwgSTd+zYEXn8xIkTTT7ttNNM9vcM3XrrrSb7e+QWLVpk8lNPPRV5fn8+/77a7KlGnPz6\n17822d9D6vP30H355Zcmv/fee5EZiBN/D+T5559vcq9evVJ6vX/9618mv/jiiyY3adLE5COPPNLk\nNm3aRL6+31f/50//mh733nuvyeyhRpRf/epXJt95550mt23b1uQvvvjC5PLycpO/973vmZzsGgRd\nu3Y1+R//+IfJ/tfv/vvvb/Lbb79t8ocffmjy6tWrTZ47d25K8/m+/e1vm9y9e/eUnp8pvFMNAAAA\nAEAgFtUAAAAAAARKuqhW1TGqulZV3630sb1VdYaqLkr8uVd2xwQKE/0BwtEfIBz9AcLRH6SqOnuq\nx4rIvSJSeUNihYjMdM4NVdWKRL4u8+NZffr0Mfmvf/2ryf4es1zzz9+uXTuTV65cGfn8oUOHmtyg\nQYPMDIZ8Gisx6U+q/D3/a9eujTz+ggsuMPmUU04xuU6dOpHZv4+0b+vWrSY/88wzJp911lmRz0dB\nGisF2p8nn3zS5Pfff9/kxx9/3OQ333zTZH+Ppr8n88ILL4w8/z//+U+TjzjiCJM///xzk0899VST\n58yZY3Ljxo1Nfu655yLPj1gYKwXSn9q1a5t87bXX5mmS3VuxYoXJ/p7qFi1amPz888+b7F/DBwVh\nrOSpP8OHDzfZ32PsX1PK98knn5ic6p7lu+++O/LxuN0nfvLkySbHdk+1c+5lEdngfbiXiIxL/H2c\niPTO8FxAUaA/QDj6A4SjP0A4+oNUhe6pbu6cWyUikvizWVUHquplqjpXVeeuW7cu8HRAUaE/QDj6\nA4SjP0A4+oMqZf1CZc65Uc65zs65zmVlZdk+HVBU6A8Qjv4A4egPEI7+lJ7Q+1SvUdWWzrlVqtpS\nRKI3W2aI/zv7Bx10UOTxLVu2NPlHP/pR5PH+fQ0XLFhg8p577mmyf983f0+nf/y+++5r8vr1602u\nV69e5HwoGnnpT6r+8Ic/mPzuu++a3KVLF5NHjBhhcqa/nv3X8/eEJnPIIYdkchzkT0H0x79v6G23\n3Wbyli1bTD7wwANN9vcsN2tm3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dJaVKvqSaq6UFUXq2pFpoYCSgH9AcLRHyAc/QHC0R/s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\nPgnCzNHsBvkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1971e1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(10, 5, figsize = (15, 20))\n",
    "\n",
    "for i, ax in enumerate(fig.axes):\n",
    "    img = X_train[y_train == 7][i, :].reshape(28, 28)\n",
    "    ax.imshow(img, cmap = \"Greys\", interpolation=\"nearest\")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = preprocessing.StandardScaler()\n",
    "X_train_std = scaler.fit_transform(X_train.astype(np.float64))\n",
    "X_test_std = scaler.transform(X_test.astype(np.float64))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying logistic regression classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.9171\n",
      "CPU times: user 11min 50s, sys: 1.38 s, total: 11min 52s\n",
      "Wall time: 11min 52s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "lr = linear_model.LogisticRegression()\n",
    "lr.fit(X_train_std, y_train)\n",
    "print(\"accuracy:\", lr.score(X_test_std, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display wrong predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of miss:  829\n"
     ]
    },
    {
     "data": {
      "image/png": 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SGCADAAAAACCJATIAAAAAAJLyfIo1AACoPsuX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\nDyVtKKm3pKHplw2VdFCxOglUK+oHSI76AZKjfoDkqJ/6ltM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SmZ0o6c0if2/MSq3cQT5I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pDNbIKZfWdmG2Z4XGMze8TMFiR/hptZgzSP7WVmK81siZktMrN3zaxTmsd2NLNVyWNX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\n2cyGmNk8M/sw9FhjM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BggAwAAAAAgiSnWsfz4449eOzwdYeHChV6uSZMmQXzi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\nEGjSpEnW/X/77bdB+4svvsjquh133DFor7/++ln3i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DMkycwaKbWE5vVcB5b+H78k0wZ1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\nGUF7+PDhPo7vQY7eHr1Xr15BrkWLFkUYHVAa8X3HUf/3f/8XtKNHQsVF9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AIKkOJ8hm9oSZfWRmWxd53C/N7F0zW2Nmb5vZZUUce5WZfW5m68zsf8zsKTM7sIjje5vZQ2a2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\nW9I9yF2cc8s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AfmLID4pAfmKykJ9qTpD/KWkPM+tlZm0knSzpwSqeP58HJQ0P2sMlTa/G\nSc3MJE2StMA5N6HW9SD1yE8E+UGRyE8E+UGRyE8E+UGRyE9EVvJjzlXvU34zO0bSf0pqJekW59w1\nVTt57vxTJR0uaWdJ70m6UtIDkqZJ6i7pHUk/cM7FN7JXopZDJD0p6WVJm4KHL1VuHX7V60H6kR+v\nFvKDopAfrxbyg6KQH68W8oOikB+vlkzkp6oTZAAAAAAA0qqaS6wBAAAAAEgtJsgAAAAAAIgJMgAA\nAAAAkpggAwAAAAAgiQkyAAAAAACSmCADAAAAACCJCTIAAAAAAJKk/w/Wx5dSHFRuigAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a312c4e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test_pred = lr.predict(X_test_std)\n",
    "miss_indices = (y_test != y_test_pred)\n",
    "misses = X_test[miss_indices]\n",
    "print(\"No of miss: \", misses.shape[0])\n",
    "\n",
    "fig, axes = plt.subplots(10, 5, figsize = (15, 20))\n",
    "misses_actual = y_test[miss_indices]\n",
    "misses_pred = y_test_pred[miss_indices]\n",
    "\n",
    "for i, ax in enumerate(fig.axes):\n",
    "    img = misses[i].reshape(28, 28)\n",
    "    ax.imshow(img, cmap = \"Greys\", interpolation=\"nearest\")\n",
    "    ax.set_title(\"A: %s, P: %d\" % (misses_actual[i], misses_pred[i]))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying SGD classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "inits = np.random.randn(10, 784) \n",
    "inits = inits / np.std(inits, axis=1).reshape(10, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 0.9209 iterations: 47\n",
      "CPU times: user 27.4 s, sys: 215 ms, total: 27.7 s\n",
      "Wall time: 8.83 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "est = linear_model.SGDClassifier(n_jobs=4, tol=1e-5, eta0 = 0.15,  \n",
    "                                 learning_rate = \"invscaling\", \n",
    "                                 alpha = 0.01, max_iter= 100)\n",
    "est.fit(X_train_std, y_train, inits)\n",
    "print(\"accuracy\", est.score(X_test_std, y_test), \"iterations:\", est.n_iter_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tu8YGgH370kwZmJXZsk8fnp91Vpqdcgot/ZEwia4hl6vq62wnTeI5NggzP7P8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\niBuqlNJ3JS2QNCKl1Cjps5IWpJRmS8qSGiQF1VAA2ou1BnQ+1hnQ+Vh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dyfC//JjNt/y3\nvy+ykZOCoqp24jtUAAAAAFAhNlQAAAAAUCE2VAAAAABQITZUAAAAAFAhNlQAAAAAUKEubflLBw+o\n77OmueqlsrXj0CTTyiVfhiVJV73pgP+Caw775Wo7dMa55/o5XHXfVt8Qdvrp59h84hjfWrhjb9nk\nNLSfvy+HepnmNknbdpcNXL3qTCuXjr7MaHC/8rnZ09TXjh3Yz9/Hib1Mq+Jef3thg9mmTT6vry+i\nq64I5q4WLS0vtyK9knts+5QtP5KkFDQlBi1Cdu6oRW/hQp+76rFFi/zYaK1GTWpO1BToXjMkf99P\nOsmPjV6oli/3uasNdY+HFD83rllu3jw/dv16n19/vc8ffbTMjrYJsic6dMg/lu7cd2tSCutb08YN\nNp80qTxXotMtav9z5ZSrVvmx0ZKqrfX5k6bs7vHH/dhLL/X5TTeVjX5nn+3HvuUtPv91w1Cbn3Za\nmdc2+ffcbfJNlq7Eszaq543a/KLxrnUuep2uFgMGSHPmlPkzz5TZLbfYKfpGNclRI+vnP19mr3ud\nHxstQPccB8cx8S+u8XP85V/63C3uj/lGu8FnPeHncIv+Xe/yY3/u2/wOvPFKm4+97btlGH1+iNbC\n4MFF1DLzVDt0+O6g1fOv/srG2xvLbOTXfAOjrrvO56/Ad6gAAAAAoEJsqAAAAACgQmyoAAAAAKBC\nbKgAAAAAoEJH3FCllCamlO5PKS1PKT2dUvpYW/65lNKLKaUlbf9UexUAUDHWGdA1WGtA52Ododq0\np+WvWdIncs5PpJQGSno8pXR329e+nHP+m3bfWk2N1LdsiNs1cVaRHQyKtiYO2mXz/3i0bAORpDPP\nLNvuep/tG/C2B6Vf28ZeUmSuJUmSRgWP6FMryjY/yZfUDBni2/w2b/ZzT55cZs8/78cO9cVHtmRM\nkvbvL5+vMWP82OYR/j66wqverslI0pbtvjqqz6nzbd5kynW6aflYx62znH1to3vMoya+qAUuOrFc\n49CSJX5s1Krk2o+amvzY5mafR+OPpjnrHN/UqYaGMtu40Y/t79dwuHjc+KjlL3oheOqpMosq2h56\nyOdRZZp53Q6fg+Nfx621ujrpggvK3J37kyb5OVautPFLZ/nXvP4ryhq9fqedbseOHeDfLzfsL98v\nrwg+1kall65UUpKuMWVlUUnrjqCY6wtfKDNTBiwpLk+Mlk/t/j1Ftqt1oB07fP86P8nO8nVm72jf\nTFx333023/s23xxWd/9PyzB64z6+ddw6691bGj26zF1j7Ny5fo6oXTZ6D/zbv23fsUnxSehaagcN\n8mOj194vfcnnrjHv8sv92LVrfe5aaqOxQQ1o/+G+CdN+EJs40Y99ImghNO/RtVFVddDuGNWDTnOv\n208E1aXtdMQNVc55g6QNbX/ek1JaLsnvSABUhHUGdA3WGtD5WGeoNkd1DVVKqV7SHEmPtEUfSSkt\nTSndlFLqlv8LBTjesM6ArsFaAzof6wzVoN0bqpRSnaQfSfp4znm3pK9JmiJptl7+vxD2+6MppQ+m\nlBanlBZviX52AICkDlpn3fRnHoGu1CFrbdu2LjteoDvisyOqRbs2VCml3np5QfxbzvlWSco5b8o5\nt+ScWyX9s6R57u/mnG/MOc/NOc8dGf1QM4COW2fRz2gDkNSBay26fgAAnx1RVdrT8pckfUPS8pzz\nlw7LD68xeJukZR1/eEB1YJ0BXYO1BnQ+1hmqTXta/s6TdJ2kp1JKv6ku+oyka1JKsyVlSQ2SPnTE\nmfr2VZ56chH3MYVkd93lp3j3FX4P+IbpQY3QgXL8gdqRdmhUnuW4EjVJ+uUvfR79FJYrLFmxwo+t\nCba/+/aVWUp+bFSy8txzPnfFa1G5V1ACY+cYLj/JyD6+lWpHq29xHHnHN8vs6qv9gRzfOm6dRdwJ\ntGWLHxv9H8EJE3zuWo6itqVo7hEjfO5EDX3Rj4a42xwwwI+NFlrtUTQARXNE99G9QERtS9GLz3zT\nCrfLr6ewUSpqSexZOm6tHTrkmx7d+Rm1/AWNp32XPe7HmzWYViz3Y4NGzbF7yze7UbPL92ZJqt3q\n31t39TOta/Kn8lmnBRV9QVXggaZyre00ja6SNPbl3oNStL5XlG9UTfVn+bHRjZrnt+7XD/qxQctY\nXd9DfrxrH9saVBAf3zpsnW3c0VdfvK08P/98w9fKwSP95zudeqrPo9d198EqqqV0VcuSNGNGmY0P\nejmC1wGdcorP3XtJ9CFs+nSfuw+brj1Qihtq16xp/3jX1ChJ0Y9Ou/fRaE3+xV/4PGj/27C5fN7H\nvvWtfo52ak/L30JJ7uP5ncd0ywD+H9YZ0DVYa0DnY52h2hxVyx8AAAAA4D+xoQIAAACACrGhAgAA\nAIAKtaeUouM0NSk1rivjQWVTwrvnv2inWLfTX9A3sc6XHOztbX5nXIs/vOh6+/37yyy41je8DtaV\nT0hSfX2ZNTT4se66f0mqqyuziy7yY6Pr3C+5xOfu+r8JvYOLlMf5Cw4HN5cXHB4a5OuGo06B0QpK\nR669tsyiO1nt3AXgUbnD0XKLJGpicRfxS/4C1OiC4T17fB5dsOoWSXTxbVQc4cZHJQ5Rc0t0cblb\n3FH9ffScRQUUTlRs4S7ElqScy+xoSkR6qgMHpCVLytw18Sxa5OdYsMDnQWGDHnqozE47zY+N3qjM\nxeu10fkWXOg+eJ6fe/BqV9rm19qeIb4laeDm8kL3/tGail5PgjIIt65Gbl95dHO7khv3GiNJ69f7\nPGqCch8iog8QVWJMv53686k/Lr9w9tvK7GRfrhKWPkSFCD82txe1fUVrp8V82Ixeexcu9LkrKZH8\n+eOOWZKuusrn7v3ypaBAZkNQ/hJ9cF5XftYPx0YlTu49LfqA/MgjPg+a2MYuu7sMow/Z7cR3qAAA\nAACgQmyoAAAAAKBCbKgAAAAAoEJsqAAAAACgQmyoAAAAAKBCKbv2ps66sZS2SHqh7T9HSAoqr3oM\n7uPx5cSc88jX+iA6G+usR+pO97Eq1pnEWuuhutN9rIq1xjrrkbrTfWzXOuvSDdVv3XBKi3POc1+T\nG+8i3Ee81qrh+eE+4nhQDc8R9xGvtWp4friP3RM/8gcAAAAAFWJDBQAAAAAVei03VDe+hrfdVbiP\neK1Vw/PDfcTxoBqeI+4jXmvV8PxwH7uh1+waKgAAAADo7viRPwAAAACoUJdvqFJKl6WUVqaUVqeU\nPtXVt99ZUko3pZQ2p5SWHZYNSyndnVJ6tu3fQ1/LYzwWKaWJKaX7U0rLU0pPp5Q+1pb3mPvY0/TE\ntdbT15nEWutuWGfdE+use+mJ60zq+WutmtZZl26oUkq1kv5R0uWSZkq6JqU0syuPoRPdLOmyV2Sf\nknRvzvlkSfe2/Xd31SzpEznnGZLOlvRHbc9dT7qPPUYPXms3q2evM4m11m2wzro11lk30YPXmdTz\n11rVrLOu/g7VPEmrc87P5ZybJH1P0lVdfAydIuf8S0nbXxFfJelbbX/+lqS3dulBdaCc84ac8xNt\nf94jabmk8epB97GH6ZFrraevM4m11s2wzrop1lm30iPXmdTz11o1rbOu3lCNl7TusP9ubMt6qtE5\n5w3SyyeVpFGv8fF0iJRSvaQ5kh5RD72PPUA1rbUeew6y1o57rLMegHV23KumdSb10HOwp6+zrt5Q\nJZNRM9iNpJTqJP1I0sdzzrtf6+NBiLXWzbHWugXWWTfHOusWWGfdXDWss67eUDVKmnjYf0+QtL6L\nj6ErbUopjZWktn9vfo2P55iklHrr5QXxbznnW9viHnUfe5BqWms97hxkrXUbrLNujHXWbVTTOpN6\n2DlYLeusqzdUj0k6OaU0OaXUR9LVkm7v4mPoSrdLur7tz9dL+slreCzHJKWUJH1D0vKc85cO+1KP\nuY89TDWttR51DrLWuhXWWTfFOutWqmmdST3oHKymddblv9g3pXSFpL+TVCvpppzz/9elB9BJUkrf\nlbRA0ghJmyR9VtJtkn4gaZKktZLemXN+5cWH3UJKab6kByU9Jam1Lf6MXv5Z2B5xH3uanrjWevo6\nk1hr3Q3rrHtinXUvPXGdST1/rVXTOuvyDRUAAAAA9BRd/ot9AQAAAKCnYEMFAAAAABViQwUAAAAA\nFWJDBQAAAAAVYkMFAAAAABViQwUAAAAAFWJDBQAAAAAVYkMFAAAAABViQwUAAAAAFWJDBQAAAAAV\nYkMFAAAAABViQwUAAAAAFWJDBQAAAAAVYkMFAAAAABViQwUAAAAAFTqmDVVK6bKU0sqU0uqU0qc6\n6qAA/DbWGgAAwPEp5Zwr+4sp1UpaJekSSY2SHpN0Tc75mejvDB8+Ik+cWF/kNWZb19p6dMfTq5fP\nW1raP0dKPm9ubv/YiLuPkj++aO7ovtTWltnRPq3R8dnHNXhyWrKf5NChMuvdu50H1uZo7k90Ljz+\n+ONbc84jj+6WX3tHu9ZGDB+e6ydOLPLd+8sHZtDuRn+jAwfauKn/YJv36WNC98RL8fnTq2+R1abg\nhSCaOzqRHbdwJOnAAZ+7OxmdyHv32rilf137D+Wll+zYQzXl4yRJvZN5oQq01vhFUtMSPK5uAQbH\n9/iqVd1ynQEAUKngo2e7zJO0Ouf8nCSllL4n6SpJ4YZq4sR63X334iLv378cG7xXh4YN8/nOnWUW\nfeayHwolbd7c/rHR3P36+dwdXzS3GytJQ4aUWVOTHxt9huzrP6PZx7X24D47dkfTCTZ3j9+YMf72\noo300Wywo3Ohpia90P5ZjitHtdbqJ07U4rvvLvK7l5SfcS+555P+FhcssPELMy+3+YQJZVa7dZOf\nO9hs7Bg2pciG9gs2Nxs3+nzAAJ+7/ysSnShLl/rc3Mk8brwdmhY+aPNdp51v80GDzBzPrbFjN9WV\nj5Mkje61rQyDhXOgzu93+u/cYHP7+K1ebYemiy7qrusMAICKHMuP/I2XtO6w/25sywB0LNYaAADA\ncepYNlSZHMfIAAAgAElEQVTuB9OKnwtJKX0wpbQ4pbR427Ytx3BzQNU64lo7fJ1t2Wa+UwEAAIBO\ncSwbqkZJh1+oMUHS+lcOyjnfmHOem3OeO3w4P1YPVOCIa+3wdTZy+PAuPTgAAIBqdiwbqscknZxS\nmpxS6iPpakm3d8xhATgMaw0AAOA4VXEpRc65OaX0EUm/kFQr6aac89Ov9ndSihvYigMLxh086PMX\nX2zfvFJczBVd5+6uoXcXkUvSpuA6/Ihr7ouKLaL77kofovKJqCnQFVtIUqMpgWsKyifGjfNzuPsT\nPdZRIcfkSf7Ad+0t7+jatX6O7upo19r+pl76daMpoFhgGtwapto5fi5fPnH5AP9ju8+vLW9v8q/K\nYgxJWnPOtTafsr0sYXhwvS9gOH+IL7aISilaxpSXnNU2+u6EDZNeb3N3bg5vCtpzZs608WDt8sfX\nWrYn1gbNLaNrgqIOmfvuyiQk9V/ysJ8iWIDPDzuzyCYvME0kAABUoWNp+VPO+U5Jd3bQsQAIsNYA\nAACOT8f0i30BAAAAoJqxoQIAAACACrGhAgAAAIAKHdM1VEcrKqVwJRHRr9JpbT263Nm50+cNDT4f\nadref/GL9t+eJL3gr3+314w/95wfGx23K6Woq/Njg2v2VV/v8xNM/8TAgX7s3Lk+d2UaU30XgoLr\n8PXsc75lY8SIMuvXz89RLQbs3aw5C/+h/MKLk8vsoovsHAuCghEd9C8Zk/uYVph3vtOOndK0x8/d\nWj6Z5w/YYIcu23aqzQcGxS3bl5bZnJn+ZDtY/PKHl7nz6sd39rVjp0/3eVTcssfczQnj/WJ9bLH7\ntWTS2LFmjt5BS05wIAdGnWjzyb80L3iPBi9IAABUGb5DBQAAAAAVYkMFAAAAABViQwUAAAAAFWJD\nBQAAAAAVYkMFAAAAABXq0pa/1lbf+LZ9e5nt3h3P4ewJisMOHCizTUHxVWTNmjKLGgHXBw1h0fGt\nW1dmgwb5sU1NPp9sytuGDPFjp0zx+ahRPnfPzYQJfuzs2T53jYNjGx62Y1c2nWPzQ4f83C+acrnR\no/3YqjF4sHTZZWW+cWMRrZE/IaZsNw+spDUHx9u8qWlomW31h3d6vwabN54wrcheemmwHeuaQV+N\naxf95nd8E9+dd/o5rr66zKJz7YYbfP65z/n8ttvK7MorfZvfWWe02PzxJWUT5oQBZgFLer7fDJtP\nvvcOf4BveEMRrdkc1H3KPFAAAPRgfIcKAAAAACrEhgoAAAAAKsSGCgAAAAAqxIYKAAAAACrEhgoA\nAAAAKtSlLX85S83NZV5bllPp1Jm+yerXS83gtrkdd3tbtvixZ5zh87Fjy+z97/djBw7wx72mwR/3\nlFGm/s/V4knSVl+btrf/yCKLWgX37/f55s0+HzeuzKIGxp07fT5sWJktG+jb/GZM9XPUyj+u69aX\nj+vevX6OatFc21fbhp1c5MMHDCiyKVuf9JMM8VWO9WP88Npf3l9kecGFduy995VtfpJvlIzaJ4Ol\noAce8PlAU0i3bZsfu2KFz9/73jKbO9ePjY7v4x/3+eteV2ZLlvixd97pX0suuMCE06fbsfue9nN/\nf9+VNn/3Az8tslEL3uwnAQCgyvAdKgAAAACoEBsqAAAAAKgQGyoAAAAAqBAbKgAAAACo0DGVUqSU\nGiTtkdQiqTnnHFyiDeBYsNYAAACOTx3R8ndhzjnotPptra2+gW2MaQ6L2vwiUUtdnz5ldvXVfuzY\nUb5J7itfK4/FzStJzzzjj/t97/PjtXZjmZk2Nknhnex/WtnyV/fsr49qjub6S23uDuXEcYfs2MeX\n9rb5M8/4Q3HuvNPnb76s1ebjxpWP9/Ll7b+9bqZda61XPqThTRvKL5iFtq51vJ1jUPDKsD9YZ3sn\nlI1+zUFb3qpVPm9oKLN3vCO4vaDJ8ZOf9LlrvYyW2X33+dwd9w03+LFRftttPj/33DKL7qNrLpWk\nu+4qs9Wrkx27YIGfY/Ron29R2eg38plH/GAAAKoMP/IHAAAAABU61g1VlvTvKaXHU0ofdANSSh9M\nKS1OKS3evj34BVAAjuRV19rh62xL9AuWAAAA0OGOdUN1Xs75DEmXS/qjlFLxqyVzzjfmnOfmnOcO\nG1b+aBqAdnnVtXb4Ohs5fPhrc4QAAABV6Jg2VDnn9W3/3izpx5LmdcRBAfhtrDUAAIDjU8WlFCml\nEyTV5Jz3tP35TZL+8tX/jtS/f5m7i697+34Dbdrk8xEjfD5sWJktWuTH7t3rCyXOPLPMTjnFz7E1\nqAyISjMmbjSlFIFDZ59v895f/YcynD7dT9LUZOMVQYGAvXi91RdETJrk51i5ssxmzPBj//3ffd5S\n408I93hPm+bn6K6Odq211vbWnrqxRb5zfTl26FA/R90+v9AGjxll81/8e1l+8B//4ed+wxt83su8\nGg0Z4se6EgdJmvD4T/wX3Hl/66126O/5GeyD9b8WBA0o533Bxp9s/L4ff8F7yuzgQTs0v68sAJF8\nmUZUCBO9jv7whz7//JWPlccx7/V+MAAAVeZYWv5GS/pxSuk383wn52x6pgAcI9YaAADAcariDVXO\n+TlJp3fgsQAwWGsAAADHL2rTAQAAAKBCbKgAAAAAoEJsqAAAAACgQsdSSnHUUvLtfdu3l9mW4HcA\nNzT4/LTTfL5rV5kNGuTHTp3q81NHbCiyx54rW9Qk6W3z/YE/1uB/B9cdy8rmvlG+SE3r/4/P/3he\n2aB96AzfwPX9oGTs0kt9fs89ZXbGGX394MD48WU2vNnXjF0z25wMkh58yNcCusfKtUZWk6Ymab1p\n9JtW92IZnjDOT9Lgz+N1TaNt7lr3du70U+/e7fPLLy+zlhY/NmqU1NZg8TQ2lllUDfqVrwSTG3V1\nPv/FL3z+HtPmJ0mzZ5dZc7Mdmrb652b+/PI1JnpdjNr/5s71uSZMKKKoGRQAgGrDd6gAAAAAoEJs\nqAAAAACgQmyoAAAAAKBCbKgAAAAAoEJsqAAAAACgQl3a8pfzyw1kr1RjtnWu+U+SrrzS5/36+dzd\n3sgR2Q92TWCS7l8xscguPG2bHZuH+Ta/uq3+Jv/r9PvLcNYsO/aHD/i5/35R2ej3sXn+Pra0JJv3\nCs6E/zL1ySJb03q6HTtl/1N+EvMk5Poz7dAUHMgg8zxK0qFDZXbKKX5stehXe0jTBpXNlNpZ1uut\nazUVjJIadvhzcNUj/jbf/vYyixrj3JqUpLGmOHNrsG7SblPfKene/efY/I1veakMo4a+6EaHDi2z\nc/zt6eqrbfxSq6k5ldR3Z9l6eWCQb1Ts3+Dr9e7bWr4+XH6uf5wmTRps88k1L9h85e4Ti2zG9OB1\nFACAKsN3qAAAAACgQmyoAAAAAKBCbKgAAAAAoEJsqAAAAACgQl1eStHcXObr15fZ/v1+jp07j+42\np0414ebNfnBrq40vHPR4Gd6y2I5NCxbYfMbOoGXDHODK7b584p57/BR33VVms2f78omo1GPfPp8P\nHTeuyPqa6/slSQf8bapv3yJKe/fYoXv7Drf5Hj9c8weWpRkPL/KlGVWjVy9p2LAifnJz2fpQ6zsL\nNG+ez+16kjR0/4tltnqZHXvookttfuBAmQ0a5G9Pm/0LwRvPDl7SGjcW0d7zL7dD66JSioMHi2jl\nvOvs0JoGP8XJ9aZFRdL3HygLKN71Lj/HlvqzbH753LIk4pFHffmEOT0kSbtGleUTkjRt48oybBzg\nJwEAoMrwHSoAAAAAqBAbKgAAAACoEBsqAAAAAKgQGyoAAAAAqNARN1QppZtSSptTSssOy4allO5O\nKT3b9u+hnXuYQM/HWgMAAOh+2tPyd7Okr0j69mHZpyTdm3O+IaX0qbb//mR7btAV6bnGqbVr/d9/\n6CGf/95FL9h85eqytWraflMrKEn9+vncVRPWBHvRG27w+WWX+Xz16iIa8ibfHDZmjJ/CPVY33+zH\nfulLPp8wtsV/4d/LNsMJpvlPkrRkic/dY/XYY3bos7N/z+Y7dvipG+vLRr8zpvux3cDN6oi1tnev\ntGhREU858w1F1tDgp+jbuMbmzX2m+L8wwZwTdXV2aO9WXxPZ+6e3lmFUN7h8uc9dVaAkXXFFEWVf\n6hlWYf66odzLjhvip4heSqLXjXfPfKoMn/atmUOmzfJzP/BAEb3+3HPt0D1NZfOmJA3s5R+/PeOm\nFVlTkz8MAACqzRG/Q5Vz/qWkV3Z+XyXpW21//pakt3bwcQFVh7UGAADQ/VR6DdXonPMGSWr796iO\nOyQAh2GtAQAAHMc6vZQipfTBlNLilNLi7du3dPbNAVXp8HW2ZVfw23oBAADQ4SrdUG1KKY2VpLZ/\nb44G5pxvzDnPzTnPHTZsZIU3B1Stdq21w9fZyMGDu/QAAQAAqlmlG6rbJV3f9ufrJf2kYw4HwCuw\n1gAAAI5jR2z5Syl9V9ICSSNSSo2SPivpBkk/SCn9vqS1kt7ZnhtLSeplbtE1/x082J4Z/9Pdq8o2\nP0m6ZJ758aeGYB/5ne/4fJZp1Ypaxq691ud79/r8lluKaPQ3v2mHfv4NZUubJJ12y2eLbH1QZLhv\nn8937661+Ylnn+3/gnHglLJxT5L6f8/cn7lz7dg593zZ5vXv+xObDx1QNsY1bvENZse7DltrJ5xg\nH9+61U8W2crV/jmbdeUEm09cZdroJGn7USziKF9jmgVPOsmPfck3BWr7Kzs92qbeeEKRDRjgp6gb\n45vp5/ReVobNQYv9v/zY5yf61ym5H9PcudMO7f3Tn/o5/vRPy+yJJ+zQplPO8XNs9z9skIaXxz0k\naDgEAKDaHHFDlXO+JvjSGzv4WICqxloDAADofjq9lAIAAAAAeio2VAAAAABQITZUAAAAAFAhNlQA\nAAAAUKEjllJ0pJoaqa6uzHfvLrOoQeotb4nntm69s8xmz/Zj3/Uun48ZU2ZTp9qhP11/ps3fPOh+\nP/cHPlBmjY1+rHugJP2XM54vwwWD/BzBA7Vyc9BW9sDtZTZsmB3av18/P4dranvmGT/WPdYK77p6\n9Sob/fbs8WOrRs5SU1MR7zmpbPT7L4PMuSNp087JNh/dp4+/zaVLy6x/fz/WtflJ0iWXlNmjj/qx\n0blWX2/jKUO2leHixX6O257zuTtnL77Yj920yecHDrR/7ujxi5oCrzGdJp/+tB3aNyjCfLzBz32S\neTmpW/6YnwQAgCrDd6gAAAAAoEJsqAAAAACgQmyoAAAAAKBCbKgAAAAAoEJdWkrR2irt31/mAwaU\n2ahRfg739yXpnnt8vuCi8kLtQUFfQ9STMGhnma3cMNKO3bvXz6GH7/b5+eeX2c9+5sf+0z/5/Pvf\nL7Px4+3QlTPfZvPt2/3UO95yfZEN3fqsHxw8sC8cHF1kO4PSkdZWn8/0XRU6dKjMxo3zY6tGa6st\npRi4cXmRtZwyw04xelU5VpK0dq3P3XPvFrYknXuuzxcuLLOZM/3Y6ISN2mzc+GiOJ57w+fTpZbbT\nvDhI8YtMVDSxYEGZRfd99Wqfv/e9RbStz1g7dPkSP8VZZ/m879KygGLbScFgAACqDN+hAgAAAIAK\nsaECAAAAgAqxoQIAAACACrGhAgAAAIAKsaECAAAAgAp1actfTY0v/mpuLrMxQavbsmU+v+8+nx88\nWGa/+IUfu2FD+2+zrs6PvfVWn+u2NT7/whfK7A//0I/90pd8/v73l1lDgx06rc/zNv+HxZNtvsYc\ndv/+J9uxUbHZokVlFhWs1df7fE79Dpuv3zm0yHp16Vl9HDp0SGpsLOI1Q84ssinLgwXVu7fPzz7b\n5089VWYDB/qxmzf7/Npry2zFCj+2Xz+fLwnq69z4K67wY4cN87lrOIxO5Kju88orfe4ElaaH3lE2\nl0r+te4Js/YkadMmn59zTnAs5gV5eL99wWAAAKoL36ECAAAAgAqxoQIAAACACrGhAgAAAIAKsaEC\nAAAAgAodcUOVUroppbQ5pbTssOxzKaUXU0pL2v4Jru4G0F6sNQAAgO6nPX1oN0v6iqRvvyL/cs75\nb47mxlpafCnWqFFl9swzfo49e3y+fr3Pv/71MnviCT/WHYfkm+euvtqPnTPqRZv/+lPf9+MferAM\ng4Y+9elj402Dyta90af5yr0fPzTaH9+v/U2ed16ZPe+LAsPytmnTyixq82tq8vljq8s2P8kXr82b\n5+foBm5WB6y13H+AXpplGv16tRTZhs2z7Bxja4IauKVLfT59ejl380g/96igTnPjxjKLTpSo7jNq\n6Js0qcweecQO3XaOb+IbPml5Gf7d3/nb+8u/tHHLCL/+amtyGbraPsXFgq7dMnjJ0IEDPq/dGjzv\nrtY0WvAAAFSZI36HKuf8S0nBWziAjsJaAwAA6H6O5Rqqj6SUlrb9mJL/9oGklNIHU0qLU0qLt2/f\ncgw3B1StI661w9fZli2sMwAAgK5S6Ybqa5KmSJotaYOkv40G5pxvzDnPzTnPHTbM/wgQgFC71trh\n62zkSNYZAABAV6loQ5Vz3pRzbsk5t0r6Z0nd96oV4DjGWgMAADi+taeUopBSGptz/s2V5W+TFFwh\n/ttqaqRBpivBXdvsLrCWpBNPbN8x/oYrOXj3u/3Y3/kdn190UZkN3bzSDx7gmy3mnLLPj//c7WX2\n6U/bods++nmb24eqqdWOXbHCH8a2bT7furXMRozwY6NiEFck8kd/5McuXuzzqIPAnU/79/ux3VEl\nay21tqjv/h3lF8wDM2LUeD/JxqAdJGpuMYUSAycH3ylbE5QZuIYRU3YhSVq1yucvvODzMWOKqHG2\nL5+Y0BSUZriT7a1v9WPdfZFU+9xzNt8y9ZwiG9nkL6f73i3+OfvYO8pCnLVr/djosP/jGV+a4To9\nWlvDn/QGAKCqHHFDlVL6rqQFkkaklBolfVbSgpTSbElZUoOkD3XiMQJVgbUGAADQ/RxxQ5VzvsbE\n3+iEYwGqGmsNAACg+zmWlj8AAAAAqGpsqAAAAACgQmyoAAAAAKBCFbX8VaqmRurXr8x37y4z1y73\nanlLS3ybr/TOd/qxl4963H/hjmfK7OGH/djrr/d50O6ln/2szG680Q4dPsTfye/fUltkv/M7Y+3Y\n6HGKHpOJE8usudmPra9vf55277Jjz6rzVYHffmKGzd/+9jLr08cfR9VoavItc3V1RdS78Xk/x4tl\nY5wkaeBAGx+aeXp5czuDXzA8bJjPx40rs6VL/dio/S9qITzllCKa8Mz9duiO2RfafOjqx9p/HK2+\nZTO67yP7HSiylj5Bm997TYOjpO/eVY435YaSpDvv9Pn1bwoaDt0Lt3txBQCgCvGOCAAAAAAVYkMF\nAAAAABViQwUAAAAAFWJDBQAAAAAVYkMFAAAAABXq0pa/lhbf6OeKr6ICqft9MZdGjvT5G99YZlHz\nlYYE7WOzZ5fZ3r1+7N13+7x3b59femmZnXqqHfoPXy3b/CR/eA0N/uamTfN5VFa2c2eZnXGGH/v0\n0z6fUbeuyH6xyNQHStq/f7DNZ83yc7sytX37/NiqUVMjDRhQxPc2TCmyc8/1UzQcnGzzFSv8+Fnl\nzWnTJr8o5+/7hZ9k7twy27/fj+0VvHS5FxjJv6AEd37oXT/xc5iWRJtJcZOheV4k2YW2ant/P1ZD\nbTp/fplNrPFtjcsn+AbBxhbfDnrAtKtGhYoAAFQbvkMFAAAAABViQwUAAAAAFWJDBQAAAAAVYkMF\nAAAAABViQwUAAAAAFerSlr+aGqlfvzJftarMDh3ycwz2JXCaNMnnrh1u9Wo/VlN9s9mc+h1m7FQ/\nR1QhGB2gqcr64//m72Rzs5/iwgvLbNbrsh07eHCy+ZNP+rn79vW5s8M8TJKkaeWTfukFB+zQb3zH\nN5tFxzdzZplFjY/Voin1VWP/k4v8jadtKbJdTf7BmrFtoc8vO9PmDy4unzfXEClJ39xqmi0l/d7W\nlUX2+SfebMd+9s+CKsf1633e2FhEefYcO3TjvKts7u7PuHH+5vr08flq81onSbt2jS6y+ZN9Q9/D\na31DnysQ/OEiP7a+3h/HSSf5vO/aZ8twVfAEAwBQZfgOFQAAAABUiA0VAAAAAFSIDRUAAAAAVOiI\nG6qU0sSU0v0ppeUppadTSh9ry4ellO5OKT3b9u+hnX+4QM/EOgMAAOie2lNK0SzpEznnJ1JKAyU9\nnlK6W9L7JN2bc74hpfQpSZ+S9MlXmyhnqbW1zN2F3Vu3+jmii8Brgq1hf9NxMGyYHztn0jab/7ph\neHl7oy6xY2cu8HN/9rM+v+66Mps3z4915R2S1NJSZv90oy+fGDTIzzF/vs+feabMzPX9kqSLL/b5\nyrVl8cG01k12bH29L6XodRT1Kdu3t3/scaTD1lmffTs04eEfll+44ooiGry/LKqQJJ13no1/fpc/\nr9xz/8ADfuro/Dn5ymlF9o53+LH//YYTbP6DH5RlHJL0uc+V2YKgP+aee3x+8GCZRespOl+jfMGC\nMtslXygxe7afwz3e75z7vB8cNfM8UWfjQ3PPKbLeO4NzBwCAKnPE71DlnDfknJ9o+/MeScsljZd0\nlaRvtQ37lqS3dtZBAj0d6wwAAKB7OqprqFJK9ZLmSHpE0uic8wbp5Q+Dksr+bwBHjXUGAADQfbR7\nQ5VSqpP0I0kfzznvPoq/98GU0uKU0uLt2/kREeDVdMQ627K73X8NAAAAx6hdG6qUUm+9/CHv33LO\nt7bFm1JKY9u+PlbSZvd3c8435pzn5pznDhtW5b9xFXgVHbXORkYX9gAAAKDDtaflL0n6hqTlOecv\nHfal2yVd3/bn6yX9pOMPD6gOrDMAAIDuqT3daedJuk7SUymlJW3ZZyTdIOkHKaXfl7RW0juPNFFr\nq7R3r89fabP9//DSSy/5/KSTfD7/vFzO0eSbyrbsLtv8JOnFF8ts4kR/e3fc4fOonXDx4jI7+2w/\ntl8/n7vHKmoTi34arE+f9t/mzJl+7HPP+XzamF1FtvCp0XZs1LA2darPBwxoX9YNdNg6O1Q3VJsu\nKIf1ay7H1vTzbXkDt/ofz21s9N9lds/bbbf544uey/e8p8yipsDLLvP5u97l86eeKrPonD/3XJ+7\nYrwT/MOnLcFPN48JmgXvu6/MTvaFhRoyxOdnnFFmewZMtmMH/uxnfpJLfHtp7+YDZdhsTigAAKrQ\nETdUOeeFkvwORHpjxx4OUJ1YZwAAAN3TUbX8AQAAAAD+ExsqAAAAAKgQGyoAAAAAqBAbKgAAAACo\nUHta/jpMSnGz1ivV1/t89myfR4VT6xrL6/wn9tpgx/bqN9bmmzaV2Zo1/vailrGxqx/0X1i2rMwW\n1dmhJ0+YYPND8y8ssvXr/c1FTXyja4JWt7qy1e322/0cV17p8z0HBxfZzp1+7O/+rs+jZjPXptbe\nc6yn6r13h0Y/8P3yC3PnFtGeUVPsHAtX+ja/P7goOPHNuXn77X3t0Isv9lO4c/Pzlz1sxz474hyb\nH81z/8wzPo/ONdemGTVeRq9fcyb4dbZmRPl4T9lvqgklqaFszXz5L5jn8gd32qF73/cRm9c1mNcj\nSYd69S+y3goWMQAAVYbvUAEAAABAhdhQAQAAAECF2FABAAAAQIXYUAEAAABAhbq0lKKmRurXr8xd\nNnq0n+PQIZ8PG+bz732vzGbN8uUTUVGCK1uIjmNs64v+C4MG+dxdiX/LLX7stdfauPeqVUV2YnC1\n/ImTxtk8j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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a18cc6cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, _ = plt.subplots(3, 4, figsize = (15, 10))\n",
    "for i, ax in enumerate(fig.axes):\n",
    "    if i < est.coef_.shape[0]:\n",
    "        ax.imshow(est.coef_[i, :].reshape(28, 28), cmap = \"bwr\", interpolation=\"nearest\")\n",
    "    else:\n",
    "        ax.remove()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.041340</td>\n",
       "      <td>-0.083744</td>\n",
       "      <td>0.038232</td>\n",
       "      <td>0.053368</td>\n",
       "      <td>-0.014164</td>\n",
       "      <td>-0.061339</td>\n",
       "      <td>-0.027433</td>\n",
       "      <td>0.109623</td>\n",
       "      <td>-0.026222</td>\n",
       "      <td>-0.051522</td>\n",
       "      <td>...</td>\n",
       "      <td>0.101004</td>\n",
       "      <td>0.041945</td>\n",
       "      <td>-0.002550</td>\n",
       "      <td>0.025023</td>\n",
       "      <td>-0.036319</td>\n",
       "      <td>0.078118</td>\n",
       "      <td>0.063360</td>\n",
       "      <td>-0.014658</td>\n",
       "      <td>-0.048840</td>\n",
       "      <td>0.044466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.117511</td>\n",
       "      <td>-0.042890</td>\n",
       "      <td>0.088114</td>\n",
       "      <td>-0.027449</td>\n",
       "      <td>0.025384</td>\n",
       "      <td>-0.047334</td>\n",
       "      <td>-0.018449</td>\n",
       "      <td>0.022574</td>\n",
       "      <td>-0.077487</td>\n",
       "      <td>-0.060981</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015102</td>\n",
       "      <td>-0.065140</td>\n",
       "      <td>-0.010722</td>\n",
       "      <td>-0.049677</td>\n",
       "      <td>-0.082337</td>\n",
       "      <td>0.033339</td>\n",
       "      <td>-0.047543</td>\n",
       "      <td>0.046065</td>\n",
       "      <td>0.016769</td>\n",
       "      <td>0.029798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.091572</td>\n",
       "      <td>-0.059533</td>\n",
       "      <td>-0.024205</td>\n",
       "      <td>0.006171</td>\n",
       "      <td>-0.005878</td>\n",
       "      <td>0.002353</td>\n",
       "      <td>-0.008457</td>\n",
       "      <td>0.008194</td>\n",
       "      <td>0.002065</td>\n",
       "      <td>-0.052933</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.037495</td>\n",
       "      <td>-0.069780</td>\n",
       "      <td>-0.064653</td>\n",
       "      <td>0.020324</td>\n",
       "      <td>-0.033194</td>\n",
       "      <td>-0.025839</td>\n",
       "      <td>-0.068084</td>\n",
       "      <td>-0.062893</td>\n",
       "      <td>-0.018241</td>\n",
       "      <td>0.004692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.012284</td>\n",
       "      <td>-0.045563</td>\n",
       "      <td>0.011654</td>\n",
       "      <td>-0.010694</td>\n",
       "      <td>-0.008686</td>\n",
       "      <td>-0.015523</td>\n",
       "      <td>0.001384</td>\n",
       "      <td>-0.023454</td>\n",
       "      <td>-0.027425</td>\n",
       "      <td>-0.021806</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.077167</td>\n",
       "      <td>-0.063370</td>\n",
       "      <td>-0.036756</td>\n",
       "      <td>-0.036734</td>\n",
       "      <td>-0.074055</td>\n",
       "      <td>0.020572</td>\n",
       "      <td>-0.016257</td>\n",
       "      <td>0.015561</td>\n",
       "      <td>-0.076205</td>\n",
       "      <td>-0.001953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.038190</td>\n",
       "      <td>-0.006219</td>\n",
       "      <td>-0.036261</td>\n",
       "      <td>-0.012859</td>\n",
       "      <td>-0.036171</td>\n",
       "      <td>0.004289</td>\n",
       "      <td>-0.046565</td>\n",
       "      <td>-0.031924</td>\n",
       "      <td>0.029226</td>\n",
       "      <td>0.020840</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001075</td>\n",
       "      <td>0.008170</td>\n",
       "      <td>-0.046674</td>\n",
       "      <td>-0.020232</td>\n",
       "      <td>0.005725</td>\n",
       "      <td>0.020468</td>\n",
       "      <td>-0.022378</td>\n",
       "      <td>-0.029770</td>\n",
       "      <td>-0.022984</td>\n",
       "      <td>-0.061402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.049574</td>\n",
       "      <td>0.103457</td>\n",
       "      <td>-0.032148</td>\n",
       "      <td>0.013034</td>\n",
       "      <td>-0.007324</td>\n",
       "      <td>-0.029559</td>\n",
       "      <td>0.008342</td>\n",
       "      <td>-0.011768</td>\n",
       "      <td>-0.064231</td>\n",
       "      <td>-0.022532</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008047</td>\n",
       "      <td>-0.011131</td>\n",
       "      <td>-0.019173</td>\n",
       "      <td>-0.003936</td>\n",
       "      <td>-0.010410</td>\n",
       "      <td>-0.006540</td>\n",
       "      <td>-0.023155</td>\n",
       "      <td>-0.057363</td>\n",
       "      <td>-0.065610</td>\n",
       "      <td>-0.003050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.023145</td>\n",
       "      <td>-0.039034</td>\n",
       "      <td>-0.067422</td>\n",
       "      <td>-0.006177</td>\n",
       "      <td>-0.088349</td>\n",
       "      <td>-0.013815</td>\n",
       "      <td>0.014393</td>\n",
       "      <td>-0.006159</td>\n",
       "      <td>-0.056585</td>\n",
       "      <td>-0.020034</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019738</td>\n",
       "      <td>0.033669</td>\n",
       "      <td>-0.042502</td>\n",
       "      <td>0.050040</td>\n",
       "      <td>-0.012148</td>\n",
       "      <td>-0.002805</td>\n",
       "      <td>-0.050735</td>\n",
       "      <td>-0.061722</td>\n",
       "      <td>-0.001923</td>\n",
       "      <td>-0.022114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.044615</td>\n",
       "      <td>0.001412</td>\n",
       "      <td>-0.006821</td>\n",
       "      <td>-0.112434</td>\n",
       "      <td>-0.009049</td>\n",
       "      <td>-0.019319</td>\n",
       "      <td>-0.079954</td>\n",
       "      <td>-0.029177</td>\n",
       "      <td>-0.073609</td>\n",
       "      <td>-0.039095</td>\n",
       "      <td>...</td>\n",
       "      <td>0.094172</td>\n",
       "      <td>0.081164</td>\n",
       "      <td>0.005558</td>\n",
       "      <td>-0.031875</td>\n",
       "      <td>0.036838</td>\n",
       "      <td>0.032705</td>\n",
       "      <td>-0.055898</td>\n",
       "      <td>-0.137616</td>\n",
       "      <td>0.006162</td>\n",
       "      <td>-0.004332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.058085</td>\n",
       "      <td>-0.038200</td>\n",
       "      <td>-0.015800</td>\n",
       "      <td>0.001676</td>\n",
       "      <td>-0.087808</td>\n",
       "      <td>0.021727</td>\n",
       "      <td>0.032674</td>\n",
       "      <td>-0.023458</td>\n",
       "      <td>-0.029830</td>\n",
       "      <td>0.031890</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035671</td>\n",
       "      <td>0.024223</td>\n",
       "      <td>0.047641</td>\n",
       "      <td>-0.001858</td>\n",
       "      <td>0.063487</td>\n",
       "      <td>-0.024790</td>\n",
       "      <td>-0.002694</td>\n",
       "      <td>-0.109103</td>\n",
       "      <td>-0.095116</td>\n",
       "      <td>-0.054177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.038191</td>\n",
       "      <td>0.022911</td>\n",
       "      <td>-0.033741</td>\n",
       "      <td>-0.012586</td>\n",
       "      <td>-0.025735</td>\n",
       "      <td>-0.023013</td>\n",
       "      <td>-0.094430</td>\n",
       "      <td>-0.014168</td>\n",
       "      <td>-0.026361</td>\n",
       "      <td>-0.053779</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116554</td>\n",
       "      <td>0.085581</td>\n",
       "      <td>0.065359</td>\n",
       "      <td>0.010903</td>\n",
       "      <td>-0.003346</td>\n",
       "      <td>0.030327</td>\n",
       "      <td>-0.065173</td>\n",
       "      <td>-0.049949</td>\n",
       "      <td>-0.014860</td>\n",
       "      <td>-0.007009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.062345</td>\n",
       "      <td>-0.002926</td>\n",
       "      <td>-0.021538</td>\n",
       "      <td>-0.067549</td>\n",
       "      <td>-0.028110</td>\n",
       "      <td>0.073723</td>\n",
       "      <td>0.015507</td>\n",
       "      <td>0.049240</td>\n",
       "      <td>-0.065391</td>\n",
       "      <td>0.004996</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064699</td>\n",
       "      <td>0.057716</td>\n",
       "      <td>0.029056</td>\n",
       "      <td>0.069066</td>\n",
       "      <td>0.079271</td>\n",
       "      <td>0.048199</td>\n",
       "      <td>0.047468</td>\n",
       "      <td>-0.042474</td>\n",
       "      <td>-0.092417</td>\n",
       "      <td>0.013372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.048833</td>\n",
       "      <td>-0.038383</td>\n",
       "      <td>0.046774</td>\n",
       "      <td>-0.084208</td>\n",
       "      <td>-0.003307</td>\n",
       "      <td>-0.108457</td>\n",
       "      <td>0.022785</td>\n",
       "      <td>-0.020041</td>\n",
       "      <td>-0.023455</td>\n",
       "      <td>0.007955</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059235</td>\n",
       "      <td>0.057132</td>\n",
       "      <td>0.077839</td>\n",
       "      <td>0.014805</td>\n",
       "      <td>0.056146</td>\n",
       "      <td>0.056287</td>\n",
       "      <td>-0.012135</td>\n",
       "      <td>-0.044334</td>\n",
       "      <td>-0.064911</td>\n",
       "      <td>-0.084577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.018899</td>\n",
       "      <td>0.037392</td>\n",
       "      <td>-0.067583</td>\n",
       "      <td>-0.010981</td>\n",
       "      <td>-0.044534</td>\n",
       "      <td>0.060382</td>\n",
       "      <td>0.034902</td>\n",
       "      <td>-0.024538</td>\n",
       "      <td>0.060499</td>\n",
       "      <td>-0.056715</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016687</td>\n",
       "      <td>0.036093</td>\n",
       "      <td>0.014532</td>\n",
       "      <td>0.093451</td>\n",
       "      <td>0.042896</td>\n",
       "      <td>-0.027390</td>\n",
       "      <td>0.039023</td>\n",
       "      <td>-0.019605</td>\n",
       "      <td>-0.065479</td>\n",
       "      <td>-0.037616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.004604</td>\n",
       "      <td>-0.024677</td>\n",
       "      <td>0.025891</td>\n",
       "      <td>-0.008763</td>\n",
       "      <td>0.005674</td>\n",
       "      <td>-0.014785</td>\n",
       "      <td>0.074007</td>\n",
       "      <td>0.125122</td>\n",
       "      <td>0.022185</td>\n",
       "      <td>0.051081</td>\n",
       "      <td>...</td>\n",
       "      <td>0.065297</td>\n",
       "      <td>0.042434</td>\n",
       "      <td>0.034566</td>\n",
       "      <td>0.023448</td>\n",
       "      <td>0.077979</td>\n",
       "      <td>0.041225</td>\n",
       "      <td>0.017213</td>\n",
       "      <td>0.015226</td>\n",
       "      <td>-0.065070</td>\n",
       "      <td>0.005889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.016111</td>\n",
       "      <td>-0.011585</td>\n",
       "      <td>-0.080387</td>\n",
       "      <td>-0.021641</td>\n",
       "      <td>0.020868</td>\n",
       "      <td>0.053959</td>\n",
       "      <td>0.041489</td>\n",
       "      <td>0.082328</td>\n",
       "      <td>0.053254</td>\n",
       "      <td>0.054171</td>\n",
       "      <td>...</td>\n",
       "      <td>0.025394</td>\n",
       "      <td>-0.040448</td>\n",
       "      <td>0.038106</td>\n",
       "      <td>0.025193</td>\n",
       "      <td>0.053142</td>\n",
       "      <td>0.038963</td>\n",
       "      <td>0.052931</td>\n",
       "      <td>-0.008306</td>\n",
       "      <td>0.015454</td>\n",
       "      <td>-0.017214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.026289</td>\n",
       "      <td>0.055688</td>\n",
       "      <td>-0.017537</td>\n",
       "      <td>-0.040345</td>\n",
       "      <td>-0.019079</td>\n",
       "      <td>0.057011</td>\n",
       "      <td>-0.020744</td>\n",
       "      <td>0.102928</td>\n",
       "      <td>0.098017</td>\n",
       "      <td>0.087180</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004238</td>\n",
       "      <td>0.044214</td>\n",
       "      <td>0.072727</td>\n",
       "      <td>0.010203</td>\n",
       "      <td>0.034125</td>\n",
       "      <td>0.030091</td>\n",
       "      <td>-0.024923</td>\n",
       "      <td>-0.016277</td>\n",
       "      <td>-0.036771</td>\n",
       "      <td>-0.021921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.020426</td>\n",
       "      <td>-0.029101</td>\n",
       "      <td>-0.092220</td>\n",
       "      <td>-0.024497</td>\n",
       "      <td>0.026500</td>\n",
       "      <td>0.029835</td>\n",
       "      <td>0.027930</td>\n",
       "      <td>0.078976</td>\n",
       "      <td>0.081492</td>\n",
       "      <td>0.089267</td>\n",
       "      <td>...</td>\n",
       "      <td>0.050439</td>\n",
       "      <td>-0.052370</td>\n",
       "      <td>0.012417</td>\n",
       "      <td>0.051899</td>\n",
       "      <td>-0.020584</td>\n",
       "      <td>0.034481</td>\n",
       "      <td>0.034716</td>\n",
       "      <td>-0.003314</td>\n",
       "      <td>-0.044267</td>\n",
       "      <td>-0.011504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.021521</td>\n",
       "      <td>-0.043468</td>\n",
       "      <td>0.003128</td>\n",
       "      <td>-0.062077</td>\n",
       "      <td>0.021757</td>\n",
       "      <td>0.056000</td>\n",
       "      <td>0.072451</td>\n",
       "      <td>0.068604</td>\n",
       "      <td>0.124729</td>\n",
       "      <td>0.126507</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.059125</td>\n",
       "      <td>0.011119</td>\n",
       "      <td>0.029977</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.026481</td>\n",
       "      <td>0.008085</td>\n",
       "      <td>0.027404</td>\n",
       "      <td>-0.019847</td>\n",
       "      <td>-0.012840</td>\n",
       "      <td>0.029878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.008192</td>\n",
       "      <td>-0.005584</td>\n",
       "      <td>0.014304</td>\n",
       "      <td>-0.022596</td>\n",
       "      <td>-0.009091</td>\n",
       "      <td>-0.032859</td>\n",
       "      <td>0.008060</td>\n",
       "      <td>0.059728</td>\n",
       "      <td>0.067544</td>\n",
       "      <td>0.075344</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020020</td>\n",
       "      <td>-0.062736</td>\n",
       "      <td>-0.021581</td>\n",
       "      <td>-0.051462</td>\n",
       "      <td>0.046664</td>\n",
       "      <td>-0.005465</td>\n",
       "      <td>-0.039817</td>\n",
       "      <td>-0.033629</td>\n",
       "      <td>0.006453</td>\n",
       "      <td>-0.016683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.012103</td>\n",
       "      <td>0.017682</td>\n",
       "      <td>-0.042669</td>\n",
       "      <td>-0.032272</td>\n",
       "      <td>-0.008426</td>\n",
       "      <td>0.009221</td>\n",
       "      <td>0.044311</td>\n",
       "      <td>0.016144</td>\n",
       "      <td>0.052833</td>\n",
       "      <td>0.012658</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.015317</td>\n",
       "      <td>-0.047048</td>\n",
       "      <td>0.001231</td>\n",
       "      <td>0.012665</td>\n",
       "      <td>-0.036442</td>\n",
       "      <td>-0.008948</td>\n",
       "      <td>0.031849</td>\n",
       "      <td>-0.028084</td>\n",
       "      <td>-0.096592</td>\n",
       "      <td>-0.082947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.029608</td>\n",
       "      <td>0.032736</td>\n",
       "      <td>-0.004839</td>\n",
       "      <td>-0.042506</td>\n",
       "      <td>-0.000894</td>\n",
       "      <td>0.001850</td>\n",
       "      <td>0.009270</td>\n",
       "      <td>0.048297</td>\n",
       "      <td>0.046153</td>\n",
       "      <td>0.078661</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.025073</td>\n",
       "      <td>-0.066429</td>\n",
       "      <td>-0.056222</td>\n",
       "      <td>-0.019679</td>\n",
       "      <td>0.070696</td>\n",
       "      <td>-0.076789</td>\n",
       "      <td>-0.027842</td>\n",
       "      <td>-0.004784</td>\n",
       "      <td>-0.001601</td>\n",
       "      <td>0.001766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.101269</td>\n",
       "      <td>-0.018611</td>\n",
       "      <td>-0.018477</td>\n",
       "      <td>0.027244</td>\n",
       "      <td>-0.006073</td>\n",
       "      <td>-0.014608</td>\n",
       "      <td>0.040325</td>\n",
       "      <td>-0.050393</td>\n",
       "      <td>-0.001603</td>\n",
       "      <td>0.078773</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013528</td>\n",
       "      <td>-0.047893</td>\n",
       "      <td>-0.039005</td>\n",
       "      <td>-0.031487</td>\n",
       "      <td>0.015348</td>\n",
       "      <td>-0.037818</td>\n",
       "      <td>-0.005486</td>\n",
       "      <td>-0.044638</td>\n",
       "      <td>0.070291</td>\n",
       "      <td>-0.002753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.025685</td>\n",
       "      <td>-0.000471</td>\n",
       "      <td>-0.119579</td>\n",
       "      <td>-0.038430</td>\n",
       "      <td>-0.055354</td>\n",
       "      <td>0.002127</td>\n",
       "      <td>0.071936</td>\n",
       "      <td>-0.065366</td>\n",
       "      <td>0.114240</td>\n",
       "      <td>0.051086</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012688</td>\n",
       "      <td>-0.005726</td>\n",
       "      <td>-0.004182</td>\n",
       "      <td>-0.006095</td>\n",
       "      <td>-0.023208</td>\n",
       "      <td>0.013783</td>\n",
       "      <td>-0.027628</td>\n",
       "      <td>-0.037346</td>\n",
       "      <td>0.026950</td>\n",
       "      <td>0.020221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.045196</td>\n",
       "      <td>0.002730</td>\n",
       "      <td>0.006022</td>\n",
       "      <td>-0.010810</td>\n",
       "      <td>-0.072160</td>\n",
       "      <td>0.032802</td>\n",
       "      <td>-0.012725</td>\n",
       "      <td>0.024191</td>\n",
       "      <td>0.016462</td>\n",
       "      <td>0.026629</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.022156</td>\n",
       "      <td>-0.071010</td>\n",
       "      <td>-0.013751</td>\n",
       "      <td>-0.011134</td>\n",
       "      <td>-0.014430</td>\n",
       "      <td>0.011322</td>\n",
       "      <td>-0.012294</td>\n",
       "      <td>-0.002119</td>\n",
       "      <td>-0.013205</td>\n",
       "      <td>-0.048735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.043836</td>\n",
       "      <td>0.105358</td>\n",
       "      <td>-0.038450</td>\n",
       "      <td>0.018631</td>\n",
       "      <td>-0.010448</td>\n",
       "      <td>-0.007475</td>\n",
       "      <td>-0.041609</td>\n",
       "      <td>0.016613</td>\n",
       "      <td>-0.038283</td>\n",
       "      <td>-0.021145</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004522</td>\n",
       "      <td>-0.028965</td>\n",
       "      <td>-0.063104</td>\n",
       "      <td>-0.072487</td>\n",
       "      <td>0.027759</td>\n",
       "      <td>-0.030358</td>\n",
       "      <td>-0.059489</td>\n",
       "      <td>-0.008950</td>\n",
       "      <td>-0.006548</td>\n",
       "      <td>0.039194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-0.003952</td>\n",
       "      <td>0.064110</td>\n",
       "      <td>-0.001676</td>\n",
       "      <td>-0.022868</td>\n",
       "      <td>-0.000894</td>\n",
       "      <td>-0.045912</td>\n",
       "      <td>-0.034020</td>\n",
       "      <td>-0.093059</td>\n",
       "      <td>-0.099881</td>\n",
       "      <td>-0.045849</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.137157</td>\n",
       "      <td>-0.058475</td>\n",
       "      <td>-0.060639</td>\n",
       "      <td>-0.021055</td>\n",
       "      <td>-0.028735</td>\n",
       "      <td>-0.005323</td>\n",
       "      <td>-0.044083</td>\n",
       "      <td>0.009620</td>\n",
       "      <td>-0.015197</td>\n",
       "      <td>0.023014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.000654</td>\n",
       "      <td>-0.014939</td>\n",
       "      <td>0.019053</td>\n",
       "      <td>-0.029181</td>\n",
       "      <td>-0.066146</td>\n",
       "      <td>0.029989</td>\n",
       "      <td>-0.054270</td>\n",
       "      <td>-0.025536</td>\n",
       "      <td>-0.034561</td>\n",
       "      <td>-0.092573</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.075142</td>\n",
       "      <td>-0.001374</td>\n",
       "      <td>-0.050646</td>\n",
       "      <td>-0.056072</td>\n",
       "      <td>-0.030210</td>\n",
       "      <td>-0.062825</td>\n",
       "      <td>-0.065299</td>\n",
       "      <td>-0.025450</td>\n",
       "      <td>0.060186</td>\n",
       "      <td>0.020327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.032131</td>\n",
       "      <td>-0.008370</td>\n",
       "      <td>0.001936</td>\n",
       "      <td>0.061819</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.035884</td>\n",
       "      <td>0.028543</td>\n",
       "      <td>-0.053902</td>\n",
       "      <td>-0.029161</td>\n",
       "      <td>0.011922</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.040846</td>\n",
       "      <td>0.008233</td>\n",
       "      <td>-0.050505</td>\n",
       "      <td>-0.031359</td>\n",
       "      <td>-0.005947</td>\n",
       "      <td>0.027312</td>\n",
       "      <td>0.032116</td>\n",
       "      <td>0.023134</td>\n",
       "      <td>-0.131759</td>\n",
       "      <td>0.048182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>28 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6   \\\n",
       "0   0.041340 -0.083744  0.038232  0.053368 -0.014164 -0.061339 -0.027433   \n",
       "1  -0.117511 -0.042890  0.088114 -0.027449  0.025384 -0.047334 -0.018449   \n",
       "2   0.091572 -0.059533 -0.024205  0.006171 -0.005878  0.002353 -0.008457   \n",
       "3   0.012284 -0.045563  0.011654 -0.010694 -0.008686 -0.015523  0.001384   \n",
       "4   0.038190 -0.006219 -0.036261 -0.012859 -0.036171  0.004289 -0.046565   \n",
       "5   0.049574  0.103457 -0.032148  0.013034 -0.007324 -0.029559  0.008342   \n",
       "6  -0.023145 -0.039034 -0.067422 -0.006177 -0.088349 -0.013815  0.014393   \n",
       "7  -0.044615  0.001412 -0.006821 -0.112434 -0.009049 -0.019319 -0.079954   \n",
       "8   0.058085 -0.038200 -0.015800  0.001676 -0.087808  0.021727  0.032674   \n",
       "9   0.038191  0.022911 -0.033741 -0.012586 -0.025735 -0.023013 -0.094430   \n",
       "10 -0.062345 -0.002926 -0.021538 -0.067549 -0.028110  0.073723  0.015507   \n",
       "11 -0.048833 -0.038383  0.046774 -0.084208 -0.003307 -0.108457  0.022785   \n",
       "12 -0.018899  0.037392 -0.067583 -0.010981 -0.044534  0.060382  0.034902   \n",
       "13 -0.004604 -0.024677  0.025891 -0.008763  0.005674 -0.014785  0.074007   \n",
       "14  0.016111 -0.011585 -0.080387 -0.021641  0.020868  0.053959  0.041489   \n",
       "15 -0.026289  0.055688 -0.017537 -0.040345 -0.019079  0.057011 -0.020744   \n",
       "16 -0.020426 -0.029101 -0.092220 -0.024497  0.026500  0.029835  0.027930   \n",
       "17  0.021521 -0.043468  0.003128 -0.062077  0.021757  0.056000  0.072451   \n",
       "18 -0.008192 -0.005584  0.014304 -0.022596 -0.009091 -0.032859  0.008060   \n",
       "19 -0.012103  0.017682 -0.042669 -0.032272 -0.008426  0.009221  0.044311   \n",
       "20  0.029608  0.032736 -0.004839 -0.042506 -0.000894  0.001850  0.009270   \n",
       "21 -0.101269 -0.018611 -0.018477  0.027244 -0.006073 -0.014608  0.040325   \n",
       "22 -0.025685 -0.000471 -0.119579 -0.038430 -0.055354  0.002127  0.071936   \n",
       "23  0.045196  0.002730  0.006022 -0.010810 -0.072160  0.032802 -0.012725   \n",
       "24  0.043836  0.105358 -0.038450  0.018631 -0.010448 -0.007475 -0.041609   \n",
       "25 -0.003952  0.064110 -0.001676 -0.022868 -0.000894 -0.045912 -0.034020   \n",
       "26  0.000654 -0.014939  0.019053 -0.029181 -0.066146  0.029989 -0.054270   \n",
       "27  0.032131 -0.008370  0.001936  0.061819  0.018443 -0.035884  0.028543   \n",
       "\n",
       "          7         8         9     ...           18        19        20  \\\n",
       "0   0.109623 -0.026222 -0.051522    ...     0.101004  0.041945 -0.002550   \n",
       "1   0.022574 -0.077487 -0.060981    ...     0.015102 -0.065140 -0.010722   \n",
       "2   0.008194  0.002065 -0.052933    ...    -0.037495 -0.069780 -0.064653   \n",
       "3  -0.023454 -0.027425 -0.021806    ...    -0.077167 -0.063370 -0.036756   \n",
       "4  -0.031924  0.029226  0.020840    ...     0.001075  0.008170 -0.046674   \n",
       "5  -0.011768 -0.064231 -0.022532    ...     0.008047 -0.011131 -0.019173   \n",
       "6  -0.006159 -0.056585 -0.020034    ...     0.019738  0.033669 -0.042502   \n",
       "7  -0.029177 -0.073609 -0.039095    ...     0.094172  0.081164  0.005558   \n",
       "8  -0.023458 -0.029830  0.031890    ...     0.035671  0.024223  0.047641   \n",
       "9  -0.014168 -0.026361 -0.053779    ...     0.116554  0.085581  0.065359   \n",
       "10  0.049240 -0.065391  0.004996    ...     0.064699  0.057716  0.029056   \n",
       "11 -0.020041 -0.023455  0.007955    ...     0.059235  0.057132  0.077839   \n",
       "12 -0.024538  0.060499 -0.056715    ...     0.016687  0.036093  0.014532   \n",
       "13  0.125122  0.022185  0.051081    ...     0.065297  0.042434  0.034566   \n",
       "14  0.082328  0.053254  0.054171    ...     0.025394 -0.040448  0.038106   \n",
       "15  0.102928  0.098017  0.087180    ...    -0.004238  0.044214  0.072727   \n",
       "16  0.078976  0.081492  0.089267    ...     0.050439 -0.052370  0.012417   \n",
       "17  0.068604  0.124729  0.126507    ...    -0.059125  0.011119  0.029977   \n",
       "18  0.059728  0.067544  0.075344    ...     0.020020 -0.062736 -0.021581   \n",
       "19  0.016144  0.052833  0.012658    ...    -0.015317 -0.047048  0.001231   \n",
       "20  0.048297  0.046153  0.078661    ...    -0.025073 -0.066429 -0.056222   \n",
       "21 -0.050393 -0.001603  0.078773    ...    -0.013528 -0.047893 -0.039005   \n",
       "22 -0.065366  0.114240  0.051086    ...    -0.012688 -0.005726 -0.004182   \n",
       "23  0.024191  0.016462  0.026629    ...    -0.022156 -0.071010 -0.013751   \n",
       "24  0.016613 -0.038283 -0.021145    ...    -0.004522 -0.028965 -0.063104   \n",
       "25 -0.093059 -0.099881 -0.045849    ...    -0.137157 -0.058475 -0.060639   \n",
       "26 -0.025536 -0.034561 -0.092573    ...    -0.075142 -0.001374 -0.050646   \n",
       "27 -0.053902 -0.029161  0.011922    ...    -0.040846  0.008233 -0.050505   \n",
       "\n",
       "          21        22        23        24        25        26        27  \n",
       "0   0.025023 -0.036319  0.078118  0.063360 -0.014658 -0.048840  0.044466  \n",
       "1  -0.049677 -0.082337  0.033339 -0.047543  0.046065  0.016769  0.029798  \n",
       "2   0.020324 -0.033194 -0.025839 -0.068084 -0.062893 -0.018241  0.004692  \n",
       "3  -0.036734 -0.074055  0.020572 -0.016257  0.015561 -0.076205 -0.001953  \n",
       "4  -0.020232  0.005725  0.020468 -0.022378 -0.029770 -0.022984 -0.061402  \n",
       "5  -0.003936 -0.010410 -0.006540 -0.023155 -0.057363 -0.065610 -0.003050  \n",
       "6   0.050040 -0.012148 -0.002805 -0.050735 -0.061722 -0.001923 -0.022114  \n",
       "7  -0.031875  0.036838  0.032705 -0.055898 -0.137616  0.006162 -0.004332  \n",
       "8  -0.001858  0.063487 -0.024790 -0.002694 -0.109103 -0.095116 -0.054177  \n",
       "9   0.010903 -0.003346  0.030327 -0.065173 -0.049949 -0.014860 -0.007009  \n",
       "10  0.069066  0.079271  0.048199  0.047468 -0.042474 -0.092417  0.013372  \n",
       "11  0.014805  0.056146  0.056287 -0.012135 -0.044334 -0.064911 -0.084577  \n",
       "12  0.093451  0.042896 -0.027390  0.039023 -0.019605 -0.065479 -0.037616  \n",
       "13  0.023448  0.077979  0.041225  0.017213  0.015226 -0.065070  0.005889  \n",
       "14  0.025193  0.053142  0.038963  0.052931 -0.008306  0.015454 -0.017214  \n",
       "15  0.010203  0.034125  0.030091 -0.024923 -0.016277 -0.036771 -0.021921  \n",
       "16  0.051899 -0.020584  0.034481  0.034716 -0.003314 -0.044267 -0.011504  \n",
       "17  0.000019  0.026481  0.008085  0.027404 -0.019847 -0.012840  0.029878  \n",
       "18 -0.051462  0.046664 -0.005465 -0.039817 -0.033629  0.006453 -0.016683  \n",
       "19  0.012665 -0.036442 -0.008948  0.031849 -0.028084 -0.096592 -0.082947  \n",
       "20 -0.019679  0.070696 -0.076789 -0.027842 -0.004784 -0.001601  0.001766  \n",
       "21 -0.031487  0.015348 -0.037818 -0.005486 -0.044638  0.070291 -0.002753  \n",
       "22 -0.006095 -0.023208  0.013783 -0.027628 -0.037346  0.026950  0.020221  \n",
       "23 -0.011134 -0.014430  0.011322 -0.012294 -0.002119 -0.013205 -0.048735  \n",
       "24 -0.072487  0.027759 -0.030358 -0.059489 -0.008950 -0.006548  0.039194  \n",
       "25 -0.021055 -0.028735 -0.005323 -0.044083  0.009620 -0.015197  0.023014  \n",
       "26 -0.056072 -0.030210 -0.062825 -0.065299 -0.025450  0.060186  0.020327  \n",
       "27 -0.031359 -0.005947  0.027312  0.032116  0.023134 -0.131759  0.048182  \n",
       "\n",
       "[28 rows x 28 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(est.coef_[0, :].reshape(28, 28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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